{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四周进阶作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据Capital Bikeshare（美国Washington,D.C.的一个共享单车公司）提供的共享单车数据，预测每天的共享单车骑行量。\n",
    "1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分）\n",
    "2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分）\n",
    "3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分）\n",
    "4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型（30分）、Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。\n",
    "5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入需要使用的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "\n",
    "# 让输出的图形直接在Notebook中显示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>0.518261</td>\n",
       "      <td>0.089565</td>\n",
       "      <td>88</td>\n",
       "      <td>1518</td>\n",
       "      <td>1606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.196522</td>\n",
       "      <td>0.208839</td>\n",
       "      <td>0.498696</td>\n",
       "      <td>0.168726</td>\n",
       "      <td>148</td>\n",
       "      <td>1362</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.162254</td>\n",
       "      <td>0.535833</td>\n",
       "      <td>0.266804</td>\n",
       "      <td>68</td>\n",
       "      <td>891</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2011-01-09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138333</td>\n",
       "      <td>0.116175</td>\n",
       "      <td>0.434167</td>\n",
       "      <td>0.361950</td>\n",
       "      <td>54</td>\n",
       "      <td>768</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.150888</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>0.223267</td>\n",
       "      <td>41</td>\n",
       "      <td>1280</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "5        6  2011-01-06       1   0     1        0        4           1   \n",
       "6        7  2011-01-07       1   0     1        0        5           1   \n",
       "7        8  2011-01-08       1   0     1        0        6           0   \n",
       "8        9  2011-01-09       1   0     1        0        0           0   \n",
       "9       10  2011-01-10       1   0     1        0        1           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "5           1  0.204348  0.233209  0.518261   0.089565      88        1518   \n",
       "6           2  0.196522  0.208839  0.498696   0.168726     148        1362   \n",
       "7           2  0.165000  0.162254  0.535833   0.266804      68         891   \n",
       "8           1  0.138333  0.116175  0.434167   0.361950      54         768   \n",
       "9           1  0.150833  0.150888  0.482917   0.223267      41        1280   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  \n",
       "5  1606  \n",
       "6  1510  \n",
       "7   959  \n",
       "8   822  \n",
       "9  1321  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "data_path = 'D:/DeepLearning_Dataset/bike_sharing_hourly/day.csv'\n",
    "rides = pd.read_csv(data_path)\n",
    "rides.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 18 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null object\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null object\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null object\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null object\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "date          731 non-null datetime64[ns]\n",
      "dayofyear     731 non-null int64\n",
      "dtypes: datetime64[ns](1), float64(4), int64(8), object(5)\n",
      "memory usage: 102.9+ KB\n"
     ]
    }
   ],
   "source": [
    "rides.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字段说明：<p>\n",
    "instant：  &emsp;&ensp;记录号<br />\n",
    "dteday：   &emsp;&ensp;日期<br />\n",
    "season：   &emsp;&nbsp;季节（1=春天、2=夏天、3=秋天、4=冬天）<br />\n",
    "yr：      &thinsp;&thinsp;&thinsp;&emsp;&emsp;&emsp;年份，(0: 2011, 1:2012)<br />\n",
    "mnth：    &thinsp;&ensp;&emsp;&ensp;月份(1 to 12)<br />\n",
    "holiday：  &thinsp;&emsp;是否是节假日（0/1）<br />\n",
    "weekday：  &thinsp;&nbsp;星期中的哪天(0 to 6)<br />\n",
    "workingday：是否工作日（是否为工作日，1为工作日，0为周末或节假日）<br />\n",
    "weathersit：&thinsp;天气（1：晴天，多云，2：雾天，阴天，3：小雪，小雨，4：大雨，大雪，大雾）<br />\n",
    "temp：    &thinsp;&emsp;&emsp;气温摄氏度<br />\n",
    "atemp：    &thinsp;&emsp;&ensp;体感温度<br />\n",
    "hum：     &emsp;&emsp;&ensp;湿度<br />\n",
    "windspeed：风速<br />\n",
    "casual：   &thinsp;&thinsp;&emsp;&nbsp;非注册用户贡献的骑行量<br />\n",
    "registered：&ensp;注册用户贡献的骑行量<br />\n",
    "cnt：     &emsp;&emsp;&emsp;&nbsp;给定日期（天, day.csv）时间（每小时,hour.csv）总租车人数，响应变量y<p>\n",
    "casual、registered和cnt三个特征均为要预测的y（cnt =casual+registered），作业里只需对cnt进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察数据分布\n",
    "rides.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.离散特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print(rides[col].value_counts())\n",
    "    rides[col] = rides[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.连续型数值特征分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001D519B17FC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001D518F3F708>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001D5193E8888>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001D5199E1948>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "rides[numerical_features].hist(figsize=(25,15))\n",
    "#sn.distplot(rides[i], bins=10, kde=True, norm_hist=False, axlabel=numerical_features[j], ax=axes[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 年份与骑行量的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d519bb0ac8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEGCAYAAABPdROvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3dd3iUVdrH8e+ZmWQmk0pCC4QSJCABpPfeu7guuroW7H2VdXcV3Wovr6uuiq5lkaIrVpDOKkgVRERFIDSBQEhvpE9mMuf9I4MGDBDSnin357pyZebkmZk7JMwv55znOUdprRFCCCFqy2R0AUIIIXybBIkQQog6kSARQghRJxIkQggh6kSCRAghRJ1YjC6gsTVt2lS3b9/e6DKEEMKnfPPNN9la62bVfS3ggqR9+/bs2LHD6DKEEMKnKKWSz/Y1GdoSQghRJxIkQggh6qTBgkQpNVcplamU2l2lLVop9ZlS6qDnc5MqX3tIKXVIKbVfKTWhSnsfpdQPnq+9pJRSnnarUup9T/tXSqn2DfW9CCGEOLuG7JHMAyae0TYbWKu1TgDWeu6jlEoErgK6eh7zqlLK7HnMa8BtQILn49Rz3gzkaa07Ai8AzzTYdyKEEOKsGixItNYbgdwzmqcD8z235wOXVWlfpLV2aK2PAIeA/kqpWCBCa71VVy4KtuCMx5x6ro+AMad6K0IIIRpPY8+RtNBapwF4Pjf3tLcGjlc5LsXT1tpz+8z20x6jtXYBJ4GY6l5UKXWbUmqHUmpHVlZWPX0rQgghwHsm26vrSehztJ/rMb9s1PoNrXVfrXXfZs2qPQ1aCCFELTV2kGR4hqvwfM70tKcAbaocFweketrjqmk/7TFKKQsQyS+H0oQQAUq2yGg8jR0kS4GZntszgU+rtF/lORMrnspJ9e2e4a9CpdRAz/zH9Wc85tRzzQDWafnNEUIAKSkpjBs3jnfffdfoUgJCQ57++x6wFeislEpRSt0MPA2MU0odBMZ57qO13gN8AOwFVgN3a60rPE91J/AWlRPwPwKrPO3/AWKUUoeA+/GcASaEECkpKbhcLlatWnX+g0WdNdgSKVrrq8/ypTFnOf4J4Ilq2ncA3appLwOuqEuNQgj/VFJSYnQJAcVbJtuFEKLenDx50nNLRrsbgwSJEMLv5OfnA+CuqDjPkaI+SJAIIfzOqevFcnNz5eytRiBBIoTwOxkZGQCUOcqrDHOJhiJBIoTwO8eOHiHU4q68feyYwdX4PwkSIYRfKSwsJCsnlwEtygE4cuSIwRX5PwkSIYRf2bt3LwD9mjmIsP58XzQcCRIhhF/5/vvvMSnoGOkiIcLBd9/ulAn3BiZBIoTwK1s2b6JzlAurGS6JdpKRmcXhw4eNLsuvSZAIIfzG0aNHST52nD5NHQD0blaOAtavX29oXf5OgkQI4Tc+/fRTLCYY2KIySCKDNZfEOFmxfBlOp9Pg6vyXBIkQwi+cPHmS1atWMqC5g4jgn+dExsaVkpuXz7p16wyszr9JkAgh/MLChQspcziY0rb0tPbu0U7ahbv5z1tv4nA4DKrOv0mQCCF83pEjR1iyeDHDW5YRF3b6+lomBVddVEhmVjbvv/++QRX6NwkSIYRPczqdPPH4Y9gtFVxxUfXLx3eNdjGwuYP58+exb9++Rq7Q/0mQCCF8ltaaOXPmcOjHw9zUqeC0uZEzzexcTESQm8cefUTW36pnEiRCCJ/10UcfsWTJEia1KaV3s5/PynrngJ13DthPOzY0SHN34kky09P4y58flvmSeiRBIoTwSStXruTVV+fQr5mD33Q8fUjrWJGFY0W/3AC2U5SLW7sU8sPuPTzyj39ImNQTCRIhhM/58MMPefbZZ+nWxMntiUWYVM0fO7BFOTM7FfHl1q089NBs2Za3HkiQCCF8hsvlYs6cOcyZU9kTmXVJAcHmC3+eMXEObu9SyHfffst9995Lenp6/RcbQCRIhBA+ITc3l/vv/z0ffvgh4+JKuatrEUF1eAcbElvOrO4FpCQf4rZbb2HHjh31V2yAkSARQni9rVu3csvNN7Fvz25uTyzkuk4lmOvh3atnUyeP9MkjXBfwwJ/+xJtvvkl5eXndnzjASJAIIbxWcXExzzzzDA899BChzlz+1iePIS3r942+pd3N33vnMbRlKe+++y533HYbBw8erNfX8He/PK1BCCEMprVm/fr1zHnlZXJyc5nWroTL4kvrNJR1LjYL3NKlmL7Nypl74Ch33HE7M2ZcwcyZM7Hb7ed/ggAnQSKE8CrJycn8618vsnPnt7QLd3N370IuinQ1ymv3bOrkychc3j9k5/333+fzz/7HXXffw+jRo1HqAk4NCzASJEIIr5CXl8eCBQtYuvRTrCY313cqYnRrxwWd2lsfwoI0N3cpZmQrBwsOVvDYY4+x+JNPuPOuu+jatWvjFuMjJEiEEIYqLS3lww8/5L3/vovD4WBkqzIujy8553InjeGiSBd/75PPhlQriw/t4e6772bYsGHceuuttG3b1tDavI0hQaKU+j1wC6CBH4AbATvwPtAeOApcqbXO8xz/EHAzUAHcq7Ve42nvA8wDQoCVwH1aNmcWwic4HA6WLVvGu+8sJC//JH2alXNlj2JiQ91Gl/YTk4JRrR0Mbulg1bEQVm7dzJYtW5gwYQLXX389sbGxRpfoFRo9SJRSrYF7gUStdalS6gPgKiARWKu1flopNRuYDTyolEr0fL0r0Ar4XCnVSWtdAbwG3AZsozJIJgKrGvt7EkLUXHl5OStWrOCdhQvIyc2jSxMX9/QpJqGR5kFqw2qGy+JLGd26jKVHQ/h8zSr+9781TJ48heuuu47mzZsbXaKhjBrasgAhSiknlT2RVOAhYKTn6/OB9cCDwHRgkdbaARxRSh0C+iuljgIRWuutAEqpBcBlSJAI4ZXKy8tZuXIl776zkKzsnMp1r3oVk9jEewPkTBHBmms7lTC5bRnLkkNYuWIZq1etZPKUqVxzzTUBGyiNHiRa6xNKqeeAY0Ap8D+t9f+UUi201mmeY9KUUqd+Iq2p7HGckuJpc3pun9n+C0qp26jsucjYphCNzOFwsGLFCv777jtk5+SSEFnBDT2K6RbtxFdPhIq2uZnZuZgpbUtZlhzC8mWfsmL5ciZNnsw111xDy5YtjS6xURkxtNWEyl5GPJAPfKiUuvZcD6mmTZ+j/ZeNWr8BvAHQt29fmUMRohGcCpB331lITm4enaJc3NSzhK5NfDdAztQ0xM2NFxczrX0pyz09lFUrVzJx0iSuvfbagAkUI4a2xgJHtNZZAEqpT4DBQIZSKtbTG4kFMj3HpwBtqjw+jsqhsBTP7TPbhRAGOjNAOke5uLVnMV2auPwmQM7U1Obmhs7FTGtXGSirVy5n9apVARMoRgTJMWCgUspO5dDWGGAHUAzMBJ72fP7Uc/xS4L9KqeepnGxPALZrrSuUUoVKqYHAV8D1wMuN+p0IIX7icrlYvXo1896eS3ZObmWA9CqmS5T/BsiZYjxDXlPPCJSp06Zx3XXXERMTY3SJDcKIOZKvlFIfATsBF/AtlcNOYcAHSqmbqQybKzzH7/Gc2bXXc/zdnjO2AO7k59N/VyET7UI0OrfbzRdffMHc/7zFidQ0OkZWcEvPIr/ugZzPqUCZ1q6UT4+GsOzTJaxauYJfz7iCq666ioiICKNLrFcq0C676Nu3r5blooWoH7t27eKVl1/iwMFDtAl3MyO+iJ4xxs+BPLmz8o364d4FxhbikVFiYvERO1szrISG2rnxppuZPn06FovvXBOulPpGa923uq/5znchhPAaGRkZvP76v1m37guibXBHYiEDW5Q3+nImvqKF3c0dXYuY0q6U9w45efnll1m6ZDF3/+5e+vfvb3R5dSZBIoSoMbfbzZIlS3j99X/jdpVzWfsSprQrxVqLXQoDUZuwCv7Uo4Bvs4P474/HeeCBBxg5ciSzZs0iKirK6PJqTYJECFEjqampPPP003y/axeXxDiZ2amIZiHes5yJr1AKejdz0j0mlxXJIXy6cT3f7fyG3//hj4wYMcLo8mpFNrYSQpzXpk2buPmmGzmQtIubLy7iD5cUSIjUUZCpctmVR/rmE8VJ/v73v/P888/jcvnOlf6nSI9ECHFWWmsWLlzI3Llz6RBRwe+6FRBjkwCpT23DKvhb7zw+Pmxn6dKlJB89yiOPPupTQ13SIxFCVEtrzQsvvMDcuXMZ3MLBw73yJUQaiMUEv+lYwh2JhezdvYu777qT3Nxco8uqMQkSIUS15s6dy9KlS5nctpTbE4sIlgn1Bje4ZTkP9DxJVkYaDzzwJ4qKiowuqUYkSIQQv7B27VoWLlzIiNgyfnNRieHXhQSSzlEu7u1WwJEff+TJJ5/AF671kyARQpymoKCAl//1Ih0iKrjx4mIJEQNcEuPkyouK+fLLrWzYsMHocs5LgkQIcZp33nmHgsJCbupcKBcYGmh8XBntI9y88vJLXn8mlwSJEOInLpeL/61eRZ+mDtqGV5z/AV7onQN2kgvNJBeaeXJnBO8csBtdUq2YTTCtbTHZObns3LnT6HLOSYJECPGT77//nvyCQga3dBhdSq0dK7JQWmGitMLEvvwgjhX57lUOPZuWExqkWL9+vdGlnJMEiRDiJ8nJyQB09OL90wNJkAnahpZz7Fiy0aWckwSJEOInGRkZBJkhIsj7zxQKFE1tFaSneveefRIkotbmzZvHpdOnk5mZef6DhU8wmUxojZyp5UXcgMns3W/V3l2d8GqLFi2i4ORJjh07ZnQpop6EhYXhcoPDN+fZ/VKJy0RoaJjRZZyTBImotVPXSeXn5xtbiKg3cXFxAKQWy2Xs3iK1NJg2bdsZXcY5SZCIWtO6ct2l7OxsgysR9aVjx44AHCn03TOd/EmxU5FR/PPPxVtJkIhaKSsro7y8HID09HSDqxH1pVWrVjRrGs2e3CCjSxHAnrzKn0OvXr0MruTcJEhErVSdFzl69KhxhYh6pZSi/4BB7M634ZSFfg33fU4QofYQunTpYnQp5yRBImrl4MGDALjCW3Lg4EHcbnnX8RfDhw+n1KnZLb0SQ7nc8E22jcFDhmKxePdQowSJqJUffvgBFWTDGdORkuJi6ZX4kT59+hAeFsrWDKvRpQS0H3KDKHHC6NGjjS7lvCRIxAVzu91s+2o75WGxVES0AmD79u0GVyXqi8ViYfSYsezMtlHikgtKjLI5zUpURDj9+vUzupTzkiARF2zXrl3k5+XiatIWbQ1DhzZl7bp1Rpcl6tHEiRMpr9B8lRFsdCkBqdCp+DbHyphx471+WAskSEQtLF26FGUJxhXVFgBH9EUcPHCA/fv3G1yZqC8XX3wx7dq2YWN6iNGlBKQv06243DB58mSjS6kRCRJxQVJSUli/fj2OmAQwV07GOpsmoCzBvPvuuwZXJ+qLUoqp0y7lx5NmUork4sTGpDVsSAuhc6cELrroIqPLqREJEnFB/v3v19HKTHls958bLcGUNe/Kxo0b+eGHH4wrTtSrcePGEWQxsz5VJt0b048FFlKKTEy7dLrRpdSYIUGilIpSSn2klNqnlEpSSg1SSkUrpT5TSh30fG5S5fiHlFKHlFL7lVITqrT3UUr94PnaS0rJUnMNaf369WzevImy2EvQQadvFlTeshvYwnnq6WcoKyszqEJRn6Kiohg2fASbM0Jk7a1G9MUJKyE2q0+crXWKUT2SfwGrtdYXAz2AJGA2sFZrnQCs9dxHKZUIXAV0BSYCryqlTvW1XwNuAxI8HxMb85sIJMnJyTz9zDO4w5pR3rL7Lw8wB1HSdjCpJ1L4v//7P7SWZcj9waWXXkqJE76SU4EbRZFTsS3Lxthx47HbfWdnx0YPEqVUBDAc+A+A1rpca50PTAfmew6bD1zmuT0dWKS1dmitjwCHgP5KqVggQmu9VVe+ay2o8hhRjzIzM/nTAw/iqICSDqNAVf7aWI9tw3ps20/HVUS2xtG6N2vXruXNN9+UMPEDPXr0oF3bNqxNDUF+nA1vU5oVZ0VlgPsSI3okHYAs4G2l1LdKqbeUUqFAC611GoDnc3PP8a2B41Uen+Jpa+25fWb7LyilblNK7VBK7cjKyqrf78bPnThxgvtmzSIrJ5eii8airT8vZ20qycVUknva8eWxPShv1pn//ve/vPHGG3LFu49TSvGry3/NkQIzPxZ4/2movsytYW2qne7dupKQkGB0ORfEiCCxAL2B17TWvYBiPMNYZ1HdvIc+R/svG7V+Q2vdV2vdt1mzZhdab8DatWsXd9x5J+lZuRQljMcdVoN/O6VwtBtMebOLee+993j00UcpKSlp+GJFgxk/fjz2kBDWHLcZXUqNlLoUNpuNGTNmYLPZKPWRiyq/zQ4is0Rx+a9nGF3KBTMiSFKAFK31V577H1EZLBme4So8nzOrHN+myuPjgFRPe1w17aKOXC4X8+bN47777qPQqSi8eArusObnf+ApSuFoN4iyuH6sX7+eW269lQMHDjRcwaJB2e12Lp0+ne1ZVrJKvf9EzxKXYurUqdxzzz1MmTLFZ67OX3kslJYtmjNs2DCjS7lgjf5bobVOB44rpTp7msYAe4GlwExP20zgU8/tpcBVSimrUiqeykn17Z7hr0Kl1EDP2VrXV3mMqKWkpCRuv/0O5s2bR3l0Bwq7XIq2RV74EymFM7Y7JZ0nkZqdzx133Mnrr79OaWlp/RctGtyMGTMwm8ysOOb9FyjaLZrly5fz8ssvs2LFCuwW75/c2Zdn4eBJM1f+5iqfuJL9TEZV/DvgXaVUMHAYuJHKUPtAKXUzcAy4AkBrvUcp9QGVYeMC7tZanzoZ8U5gHhACrPJ8iFrIzMxk3rx5rFy1ChVsp/SiUbii4+v8vBURsRQmXob1+Ne89957/O+zz7nt1lsYO3YsZrNc6OYrmjZtypSpU1m+7FOmti2laYj3zn2FWDRlRWV8/PHHlfejvDtItIaPj4QS3SSKKVOmGF1OrRgSJFrr74C+1XxpzFmOfwJ4opr2HUC3+q0usOTk5PD+++/zySeLcbndlDdPxNG6F5jrcY0lixVH/FBcTRNwH/+Kp556iv++9x4333QTQ4YMkUDxEddccw0rV67gkyMh3JZYbHQ5fmN3bhD78y3ce+/1WK2+eZq17/WhRL04fvw4ixYtYvWaNVRUVOCM6YijVa/TzsqqbxXhLSjuMg1L3lGSU3fyt7/9jVatW/Pbq69m3LhxPvufKFA0b96cX/96Bu8vWsS4uDLiI+QqxbqqcMN/fwyjVWxLpk6danQ5tSZBEkBcLhfbtm1j8ZIlfLNjB5jMlMckUN6yG9oW0ThFKIUrOp7CJu2w5CWTkv4Dzz33HP9+/XWmTZ3KtGnTaNWqVePUIi7Ytddey+qVK1h4sIK/9M7H5Bvz2F5r7QkbJ4pMPPbg3QQH++5KyxIkASAlJYU1a9awctVqcrKzwBqKo3VvnM06o4MMmjxVJlzR8biatMdcmIYzcx/vLVrEe4sW0bdvXyZNnMjQoUOll+JlwsLCuPPue3jqqaf44oSVMXEOo0vyWdllJj48Ekr//v0YOnSo0eXUiQSJn8rPz2fTpk2sWr2avXv2VPYEIlrh7DgGV1Sbn65ON5xSVES0oiKiFY7yYoKy9rNjVxI7vv6aELudsWPGMHbsWLp3747J5CU1B7jx48fzvzVr+GDXTi6JcdLMiyfevZXWMG9/GJiD+f3v78fXlwmUIPEjRUVFbNmyhbVr17Jjxze43RXokCjK4/rijOmIDvbutXt0cCjlrXtT3qpXZS8l+xDLV65i2bJlNImOZszo0YwePZouXbr4/H88X6aU4o9/+hM333QjbySF81CvkzLEdYHWnrCyKyeIWbPuIjY21uhy6kyCxMfl5+ezZcsWNmzYwDfffENFRQXYwnE0T8QV0wF3SDT42ptulV5KWcUgLPnHceYe5qNPFvPRRx8R07QZI0cMZ/jw4XTr1k3O+jJAbGws9836PU899RRLj4ZwWbxcH1RTx4vMvPdjGAP692f6dN9ZKv5cJEh8UFpaGlu2bGHz5s18//33lYsj2sIpb9oFZ3R73KHNfC88zsYchCumA66YDpS5HFjyj5Gel8zHi5fw8ccfExEZxbChQxg6dCi9e/eWOZVGNH78eL7++msWr/2cTlFOEpu4jC7J65W64OU9kYRHRPHg7Nl+07OWIPEBWmsOHDjAl19+ycZNmzhy+HBlu70J5S0vwdWkPW67D/Y8LpTFiqtpAq6mCZRVOLGcPI4zN5mVaz5jxYoVWK02+vfvx7BhwxgwYACRkbW4Il/UmFKK+++/nwP79/HaXnikbx7RVpkvORutYe6+MDJLTDz/xD+Ijo42uqR6I0HipRwOB999911lz2PLFnJzciqHfMJa4GzTD1dUu8Y7ZdcbmYNwRXfAFd2BMncF5sI0yvOOsXn7TjZt2oRSiq7dujF0yBCGDBlCmzZtzv+c4oLZ7XYeefQx7rzjdl7ZHcHDvfKxyDkR1Vp93MZXmVZuvfUWevbsaXQ59UqCxIvk5+ezbds2tmzZwvbtX+NwlKHMQZRHtMYVn0hFZBt0kG+swNqoTGYqIuOoiIzDoTWmkhws+cf44fBxdv/wb/7973/TOi6O4cOGMXjwYBITE2VepR7Fx8cz+6GH+cc//sE7B0O5obNc9X6mvXkW3v8xlOHDh/Hb3/7W6HLqnQSJwdLS0ti8eTMbNm5kz+7dlfMd1lDKI9rjimpDRUQsmOTHVGNK4Q5tSnloU8pb90Y5irDkH+NY/jHeW/Q+7733HuHhEQwdOoRhw4bRp08fmVepByNHjuSqq65i0aJFxIe7GNFKri85JbvUxJw9kbSJi2P27If8Zl6kKnmHMkBycjIbNmxg/YYNHP7xRwC0PZry2B64otritsf4/3xHI9HWMJwtEnG2SARXOZaTKTjzj7H6s7WsWrUKq83GwAEDGDFiBAMHDvSp7U29zS233MLBAweY/91O2oS56CBLqFBeAS/viaDCbOPxJ5/y298vCZJGkpKSwrp161i37guOHj0CgDusOeVx/XA1CfD5jsZiCf75DLCf5lWS2bj1azZs2EBQcDCDBw1i1KhRDBw4EJtNhhEvhMVi4a9/+xu33XoLr+yBR/vmEhbk3SvvNrR3D4ZypMDM44//xa/n6SRIGlBhYSHr1q1j1erV7EtKAsAd3oLytgNxNWnv9RcI+rUz5lXMRRlYco/8FCq2kBBGjxrFxIkT6d69u18ORzSEqKgoHn3scX53z928kRTG77sXBmzn+sv0YL5ItfHb3/7W55dAOR8JkgaQlJTExx9/zPr1G3C5nGh7Exxx/XDFdEAHhxpdnjiTUlSEt6QivCWOtgMwF6bjzPmRVWs+Y+XKlcTGtuKyy6YzefJkwsPDja7W61188cXcedfdvPTSS3yWYmN8mzKjS2p0GSUm5h0Ip3u3rtx0001Gl9PgJEjqidvtZuPGjby3aBH79+1DWYJxRHfE2TRB5jx8iTL9fFV924FY8o5yIvsgr732GnPnvs2kSRP5zW9+4xfLWjSkX/3qV3y9fTuLtm8jsYmTuLDAmS9xa3g9KQKL1c5f/vo3n9zx8EL5/3fYwLTWbN++ndffeKNy4jwkgrK2A3E2TQBzkNHlibowB/10AaSpOAdn5l6WLF3GsmXLuPTSS7n22muJiYkxukqvpJTigQcf5IaZ1/PWvgr+2jsfc4BcX7LmuI1DJ8385S/306JFC6PLaRQB8qNtGPn5+fz1r3/lwQcf5PCJTErjh1PY9fLKM4QkRPyKOzSGsvhhFHWfQWl0RxYvWcK1117HmjVrKk/ZFr/QpEkT7r1vFocLzHx+IjBOXMgqNfHRkVAGDx7MmDHVbvjqlyRIamnv3r3MvOEGNn+5FUdcXwq7Xo6raUfvWZ69gVmPbcNckoO5JIeQfSuxHttmdEmNQgeH4mg/hKJul1NsieCpp57isccew+WSdaaqM3r0aPr27cPio6EUlPv/8O6iQ3ZMlmBmzZoVUCdo1OhdTyl1X03aAsXBgwf5wx//yMkyN8VdplEeewmYAutKaVNJLqrCiapwYilMx1SSa3RJjUrbIinuPBFH696sW7eORx99rHLlZXEapRS/+929ONwmFh/x77MU9+db+DrLyjXXXEvz5s2NLqdR1fTP55nVtN1Qj3X4DLfbzRNPPElphYmiThMrF0sUgUmZKG/Vk7K4fmzcuIHPP//c6Iq8Urt27Zg8eQrr02xkl/lvj/2TI6E0iYrkyiuvNLqURnfOn6pS6mql1DIgXim1tMrHF0BO45ToXXbu3MnRo0cobdUbbQ0zuhzhBZwtu6FDY1j0/vtGl+K1rrvuOkwmM8uTDdrauYHtz7eQlGfhmmuvC8gLWc931taXQBrQFPhnlfZCYFdDFeXN0tLSACrXwBICQCmcYS1ISz1idCVeq3nz5owdN551/1vFjA4lfnfF++rjNsLDQpk6darRpRjinD0SrXWy1nq91nqQ1npDlY+dWuuAnF2MiooCwFRWYHAlwpuYygqJjJL9T87liiuuwFEBG1L9a5HMrFITO7OsTL/sVwHZG4GaT7ZfrpQ6qJQ6qZQqUEoVKqUC8p20crVYG8Hpuyt3qhEBz1SSi6XgBCOGDze6FK/WoUMHLunejQ3pdr/6r7MhzQpKMW3aNKNLMUxNZ76eBS7VWkdqrSO01uFa64BcZdBut3PzzTdhOXmcoKx9RpcjjFZRjv3oJsLDw7jmmmuMrsbrTZk6jfRixf58/7gW2q1hU7qd/v36BczFh9WpaZBkaK2TGrQSH3L55ZczYMBAbMe2Yck5bHQ5wigVTuyH1mIuzecvf/6zbO1bA8OHD8dmtbIlveGGt9qGuQgxuwkxu7k4yknbsIYbhU/Ks5BXBhMnTWqw1/AFNQ2SHUqp9z1ncV1+6qNBK/NiFouFRx75B926dSPk8HqC0vfIMFeAUc5SQvevIqgog9mzH2TAgAFGl+QTQkJCGD5iBNuzbZQ30GU313YqoV14Be3CK3i4dwHXdippmBcCvky3Yg+xMXjw4AZ7DV9Q0yCJAEqA8cA0z0edTk9QSpmVUt8qpZZ77kcrpT7zzMV8ppRqUuXYh5RSh7J9QhAAAB1WSURBVJRS+5VSE6q091FK/eD52kuqES8ltdls/PO55xg2bBi2419hO7IJ3AF5/kHAMRVlEpa0FKuzgMcff5zx48cbXZJPGT9+PKVO+D4n2OhS6qS8AnZk2xgxclTA77JZ0yAxAb/XWt+otb4RuL8eXvs+oOpw2WxgrdY6AVjruY9SKhG4CugKTAReVUqduoz8NeA2IMHzMbEe6qoxq9XKI488wsyZMwnK/ZGwpOWYSvIaswTRmLQmOG0XoftX0qJJOK/OmRPwf4nWRs+ePWkSFcm2DN8Oku9zgil1EVBrap1NTYPkEq11/qk7Wus8oFdtX1QpFQdMAd6q0jwdmO+5PR+4rEr7Iq21Q2t9BDgE9FdKxQIRWuutunLVvAVVHtNoTCYTN954I888/TSRQW7CkpYSlL4btLuxSxENSDkKsR9YjTVlB8OGDuWtN98kISHB6LJ8ksViYdToMXyXY6XU5bvrUW3NCKZJVCS9etX6rdBv1LhHcsZQUzR1W4L+ReABoOq7bQutdRqA5/OpxWpaA8erHJfiaWvtuX1m+y8opW5TSu1QSu3IysqqQ9lnN2DAAObPe5uBA/pjO76d0P2rUGUnG+S1RCPSmqDMJML3LMFenscDDzzAo488Ihtc1dGYMWNwumFHlm/2Soqdiu9zrIweMxazObDW2atOTYPkn8CXSqnHlFKPUnnF+7O1eUGl1FQgU2v9TU0fUk2bPkf7Lxu1fkNr3Vdr3bdZs2Y1fNkL16RJE5588kkefvhhwtxFhO9ZQnDqd+CWxfx8kak0n9D9K7Elb6VXz0uYP28ekydPDqhVXRtKYmIisS1b8GWGb84t7MgKxumGsWPHGl2KV6hRkGitFwC/BjKALOByrfXCWr7mEOBSpdRRYBEwWin1DpDhGa7C8znTc3wK0KbK4+OAVE97XDXthlJKMX78eBYuWMCwoUOxnthJWNIyzIXpRpcmasrtIvjETkL3fkq4u5gHH3yQfz73HC1btjS6Mr+hlGL8hInszQ0i1+F7Czl+mWGjdatYLr74YqNL8Qo1/glqrfdqrV/RWr+std5b2xfUWj+ktY7TWrenchJ9ndb6WmApP68yPBP41HN7KXCVUsqqlIqnclJ9u2f4q1ApNdBzttb1VR5juJiYGB599BGefPJJmoWase9bifXIJpSz1OjS6kdFOTabjRkzZlQuC1FRbnRF9cJ8MoXwPUuwpn7HmFEjWbhwAZMmTZJeSAMYN24cGtia7lvDW1mlJpLyLEyYKL8Xp3jT5aVPAx8opW4GjgFXAGit9yilPgD2Ai7gbq31qbGiO4F5QAiwyvPhVQYPHkyvXr2YP38+H3zwIdaTxyht1Qdns04+vQmWcpUz9dKp3HPPPWit+WDZGqNLqhPlKMJ2/Cssecm0at2aP9z/d/r06WN0WX4tLi6OromJbErew+S2ZfjKe/KpiynHjRtncCXew9Ag0VqvB9Z7bucA1Z5Hp7V+AniimvYdQLeGq7B+hISEcMcddzBhwgRefPFFvv/+S4KzD1DadiDuMN/cAEdbglm+fDlaa1asWIG2+OimRe4KgtN/wJa+C4vZxA233soVV1xBcLBv/ZXsqyZNnsxzz+3lxwILHSO9/zqsyiVRQujduxexsbIC+Cm++yexD4qPj+fFF1/kr3/9K9HBbkKTlmM9stk3h7vMwZSVlfHxxx9TVlYGZt974zWfTCF87xKsJ3YydPAgFi5YwDXXXCMh0ohGjRqF1RrsMysCJ+VZyCpVTJo02ehSvIoESSNTSjFmzBj+++47/OY3v8GW+yPhez4hKHOfXHvSSJSjiJBDa7Ef+B+tosN49tlneeyxx2Qy3QChoaGMGjWabVk2Sr2/Q8L6VBthoXaGy0rPp5EgMYjdbufOO+9k7tz/0KNrF2zJXxK6bwWm4myjS/NfbjfBabsI37MYe3E6t956K/Pefpv+/fsbXVlAmzZtGg4XbPPyU4ELyxXfZFsZP2FiwC+JciYJEoO1b9+eF154gb/85S9EW1yEJi3DeuwrqHAaXZpf+Wl9rJQdDBk0gIULZRjLWyQmJtKhfXvWp3n3Nryb0q243AT0viNnI0HiBZRSjB07lnfeWcil06YRnLmX8D2LMZ88YXRpvq/CiTV5G6FJy2lqN/Pkk0/y+OOPB/TeEd5GKcWll13GkQIzhwu88ypxt4YvUu1069qV+Ph4o8vxOhIkXiQsLIz777+fOa+8QlzzJtgPrMF6dLP0TmrJXJhO+N5PCc7cy+WXX87CBfNlkUUvNW7cOGzWYNae8M6tavfmBZFRoph+WaMv5+cTJEi8UNeuXfnPW29x9dVXE5x9kPCkTzEVNcwaYX5Juwk+sRP7/lW0iLLzr3/9i3vvvRe73UdPUQ4AoaGhjBs/gW2ZNoqd3ndBydoTViIjwhkxYoTRpXglCRIvZbVauf322/nXiy8SE2oldP/KyjO7xDkpZ2llTy71OyZOmMDbc+fSo0cPo8sSNXDppZfirKici/AmuWUmvs22MnnKVJlTOwsJEi/Xo0cP3p77H/r26Y0t+UusyVtlN8azMJXmE5a0DFtpNrNnz2b27NnSC/EhCQkJJHa5mC9S7V71K74+1YqmMuhE9SRIfEBERATPPP00V155JcGZSdgOr5drTs5gKs4mbP9KIkMsvPzyy0yc2Kh7nIl6Mv2yX5FWrEjK847Vm1xuWJ9up3+//nIl+zlIkPgIs9nMXXfdxe23305Q7hFsR7dIz8TDVJpP2MH/0bRJBK/OmSMrsvqwkSNHEh4W6jWT7t/lBJNfBpdOn250KV5NgsTHXH311ZVb+2YfJChjj9HlGK/CSeiPawm323jh+edp3bravc2Ej7BarUyaPIWd2VZOlhs/6b7uhI1mTaMZMGCA0aV4NQkSH3TDDTcweMgQbCd2YCrNP/8D/Jj1+NdQVsCjj/yDuLi48z9AeL0pU6ZQoWFTmrGT7lmlJnbnBjF5yjQsFu8YavNWEiQ+SCnFA3/6EzarleATNd1o0v+ospMEZ+/nV5ddRs+ePY0uR9STdu3a0b1bVzamGzvpvjHNilIwebIs0Hg+EiQ+Kioqist/9SuC8o/55urB9SAo+yAmpbj22muNLkXUs8lTppJerDhw0piegFvD5gw7ffv2lVUQakCCxIeNGDECtMZcYPgOw4YILkile/dLiImJMboUUc9GjBiBzWpls0HDW0l5FnJKkeXia0iCxIe1b98eAJOj0NhCDGIqLyQ+vr3BVYiGYLfbGTlqFNuzbJRXnP/4+rY53UqoPYQhQ4Y0/ov7IAkSH/bzftHGn91iFNkz23+NHz+eUhd8m924V5M7KuCb7BBGjhoty8XXkASJD0tNrRzScgcH5tXbOsjOiROBOawXCHr06EHTmGi+bOR9SnZmBVPm0owdO7ZRX9eXSZD4sG++qTxjq8JH932vq/LQ5uzatQuHw2F0KaIBmM1mRo8Zy67cYIoacSHHbZlWmsZEyxptF0CCxEdprVm2fAU6tCnaFml0OYZwRXegrKyU9evXG12KaCBjxoyhwg1fZzbO8FaRU7ErN5jRY8ZiMsnbY03Jv5SP2rx5M0ePHKaseRejSzFMRXhLtD2a+QsW4HL5wIbf4oJ16tSJ1q1i+SqzcYa3dmQFU+GuDDBRcxIkPsjhcPDKnDlgb4Ir5iKjyzGOUpS26k3qiRMsXrzY6GpEA1BKMWbsOJLyg8h3NPzw1rYMG61bxdKpU6cGfy1/IkHig+bOnUtGejolbQaCCuwfYUVUG1yRbXjzzbdISUkxuhzRAMaMGYPWNHivJM9Ruerw2HHj5WzACxTY70I+6Ntvv+X9Dz6gvFlnKiJkWWuUoqzdIJxueOLJJ2WIyw+1a9eOjh0vYmtGw64I/FVG5b4jo0ePbtDX8UcSJD4kPz+fRx97HGwRONr0N7ocr6GtYZS0HUTS3r3MmzfP6HJEAxg/fgKHC8ykFTfcW9aWjBA6d0qgXbt2DfYa/kqCxEdorXn66afJy8+nOH4kmIOMLsmruGI6UN40gXfeffen06KF/xgzZgwmpdjcQNvwphSZSS40MX6CbIhWG40eJEqpNkqpL5RSSUqpPUqp+zzt0Uqpz5RSBz2fm1R5zENKqUNKqf1KqQlV2vsopX7wfO0l5ccDm4sXL2bbtm2UxfXDHSprS1XH0XYghETx2OOPk58f2Mvr+5uYmBj69uvLlgw77gZYEXhTuhWz2SRna9WSET0SF/AHrXUXYCBwt1IqEZgNrNVaJwBrPffxfO0qoCswEXhVKWX2PNdrwG1AgufDL/+cSE5O5rXXXsMV1QZnAJ/ue17mIIrjh3PyZAH/fP55o6sR9WzixEnklsGe3Prtjbvc8GVGCIMGDSIqKqpenztQNHqQaK3TtNY7PbcLgSSgNTAdmO85bD5wmef2dGCR1tqhtT4CHAL6K6VigQit9VattQYWVHmM39Ba83/PPYcLE2Xth4L/drrqhdseQ1mrnmzauJHNmzcbXY6oR0OGDCE8LJQN9bwi8Pc5QZx0wOTJU+r1eQOJoXMkSqn2QC/gK6CF1joNKsMGOLXuR2vgeJWHpXjaWntun9le3evcppTaoZTakZWVVZ/fQoPbvHkzu3/4gdLWfdFBIUaX8xO3PRptDkKbg3CFt8Rtjza6pJ+Ut+iOtjfh1ddek7O4/IjVamX8hInszLZSUI/b8K5PDSEmugn9+8sJLLVlWJAopcKAj4FZWuuCcx1aTZs+R/svG7V+Q2vdV2vdt1mzZhderIHeefddCInE2TTB6FJO42g7kAp7DBX2GEovnlw5P+EtTCbKWvUi9cQJ6ZX4malTp+Jyw5Z6mnTPLjOxKyeIyVOmyna6dWBIkCilgqgMkXe11p94mjM8w1V4Pmd62lOANlUeHgeketrjqmn3G6mpqezft4+ypp0C/sLDC+WKaouyhvL552uNLkXUo/j4eLp1TeSLtPrZhndjqhVkO906M+KsLQX8B0jSWledEV0KzPTcngl8WqX9KqWUVSkVT+Wk+nbP8FehUmqg5zmvr/IYv7Bv3z4AKiKqHbET56JMlIe1ZG9SktGViHo2ddqlpBcr9ufXrQdR4YaN6ZXb6cbGysW9dWHEn7lDgOuA0Uqp7zwfk4GngXFKqYPAOM99tNZ7gA+AvcBq4G6t9ak90+4E3qJyAv5HYFWjficN7NQprN40N+JL3EF2Tp6U04D9zciRIwm1h/BFat2udN+VG0RuGUydOq2eKgtcjT4oqLXezNm39Kv2JG6t9RPAE9W07wC61V913iUiIgIA5SyVMKkFk7OEsLBwo8sQ9cxmszFu/ARWLF1CsbOY0KDajXFtSLURFRkh2+nWAxl492KXXHIJAJa8ZIMr8UHuCoILU+nTu5fRlYgGMGXKFJxu2JpRu31KTpYrvssJZuKkyTLJXg8kSLxY8+bN6de/P7bMvShnmdHl+JTgjD3o8lKmTJFrA/xRQkICF3WIZ3N67XrqX6ZbcWuYNGlSPVcWmCRIvNxdd96JSbsIObwetNvocnyCqSgTa+q3DB48hD59+hhdjmggEydN5nCBmRPF5vMffIYvM0Lo3LmTLNBYTyRIvFx8fDz3338/5oJUbEc2SZich6kkh7BDn9OieTMefPABo8sRDWj06NGYlGLbBQ5vnSj2LNA4fsL5DxY1IkHiA6ZMmcJNN91EUM6PhPy4DiqcRpfklcwFaYTtX010ZDj/fO45IiMDcy/7QBETE0PPXj35Kivkgq4p+SojGJNSjBw5ssFqCzQSJD7i+uuv55577iEo/zhh+1agyk4aXZL30Jqg9D3YD6ymdWxzXnn5JeLi4s7/OOHzRo4cRXqxIuUChre+zrbR/ZLuxMTIKtr1RYLEh8yYMYNnn32WMFVO+N6lWLIOUC+X9/ow5Swl5NDn2I5/xaCBA3n93/+Wi8sCyNChQ1FKsSOrZsNbacUmThSZGDFiZMMWFmAkSHxMv379ePvtuVzSLZGQo5sJOfQ5qrzY6LIan9ZYcg4TvmcxtqJ0fve73/HEE08QGhpqdGWiEUVHR5OY2IXvcmq29tZ3OZWBM3jw4IYsK+BIkPig5s2b8/zzz3PXXXcRUpJB+J7FBGUmBUzvRDmKCDm0lpDD60no0I433nidX//615hM8usciAYPHsKRAjP5jvOvCPxdjpX49u1o2bJlI1QWOOR/no8ym81ceeWVzHv7bXp074oteSuh+1ZgKs4xurSG43YTlPYD4Xs+wV6SwR133MGrc+bQoUMHoysTBjq1/Pvu82x4VeaCAyctDBg4qDHKCigSJD6udevWvPD88/z5z38mylxOaNJSrMlbweUwurR6ZS5IIyzpU2wpXzOgX18WLJjPVVddJVclCy666CKiIiPYnXvueZJ9+UFUuCuHh0X9kv+FfkApxbhx4xg4cCBz585lyZIlWPOOUNq6b+U+Jj68q6IqL8Z6fDtBuUdo3rwFs/7ypIxvi9OYTCZ69e7Dd1u/QOuis/66780LIijIQrdufrs8n2GkR+JHwsPDue+++3jjjTdI7NQR29HNhO5bjqnIt3aFBCrXykr7nvDdH2MvTGHmzJksXLhAQkRUq1evXuSVQXrp2d/SkvKtdE3sitVav1v1CumR+KWEhAReeeVlPvvsM1599TXyk5ZR3qwTjri+YKnb0tuNwXzyBPbj26D0JIOGDOF399wjp/SKc+rRowcAB/KDiLX/cli31KU4VmRiZM+ejV1aQJAg8VNKKcaPH8+QIUOYN28eH3/8Mdb8Y5S27oOzaSevHO5S5cVYj31FUN5RYlu1YtY/HmbAgAFGlyV8QNu2bYmMCGd/fhkjWv0ySA6etKA1dO/e3YDq/J8Mbfm50NBQ7r77bt566y26XpyA7eiWyrO7SnKNLu1n2k1Q+h7Cd3+CvegEN910E/PnzZMQETWmlKJrt+4cKqx+2OrQSQsmpUhMTGzkygKDBEmA6NChAy+/9BKzZ88mQpUSuncpwSk7wO0ytC5TSQ5hScuxHf+Kfn16MX/+fK6//nqCg2u3z4QIXF27diW9WFHk/GVv+3CBhfbt2mK32w2ozP/J0FYAUUoxceJEBg0axGuvvcbq1aux5idT0m4IFeGNfIGWu4Lg1G+xpv9AZGQks/7+d0aOHInywiE34Rs6d+4MwNHC09/WtIbDRVaGD+xqRFkBQXokASgyMpLZs2fz3HPP0Tzchn3fSqzHtzda78RUnENY0lKsabuYOGEC7yxcyKhRoyRERJ2cCpIjBacv4JhdZqKoXP/0dVH/JEgCWN++fZk3722mTZtGcPpuwpKWYyrJa7gX1JrgtF2EJi2jiVXxzDPPMHv2bMLDZV91UXfh4eG0bNGc5KLTeySn7nfq1MmIsgKCBEmAs9vt/OEPf+CZZ54hMshNaNJSgjL31fu6XcpZiv3AGqwpOxg+bBgL5stkuqh/CZ06c6z49Pm1Y4VmTEoRHx9vUFX+T4JEADBgwADmvT2XPr17YUv+snI3xnoa6jIVZRKWtBRbaRZ//OMfeeSRfxAREVEvzy1EVR06dCCjWOGu8nfQ8WIzrVrFYrN5/zVUvkqCRPwkOjqaZ595hhtuuIGg3B8J3b8KVV5Sp+e0ZB8idP8qWjQJ59VXX2Xq1KkyFyIaTHx8PBqItrlpG1b5h9CJkmA6XNTR2ML8nASJOI3ZbOaGG27g8ccew1ZeQNi+5ajS6ndjdNujcdujq38irQlO/Y6QIxvpcUl33nzjDRISEhqwciGgXbt2APSMKefaTiU43ZBZon5qFw1DgkRUa+jQobzyysuEW82EH1hZ7QWMjrYDcbQd+MsHa4015WusJ3Yybtw4/vncczKUJRpFXFwcJqVIK6k8cyuz1IxbV175LhqOBIk4q06dOvHqnFdoEm4n7OCas/ZMzhR8YifB6buZPn06Dz30kCz1LhpNUFAQzZs3I8MTJBkllW9xcXFxRpbl9yRIxDm1adOGF194gXBbMGGHPkM5y855fFDWfqxp3zN58mRmzZoluxaKRhfXpg0ZZZV/vGSWVgZKq1atjCzJ7/n8/3Kl1ESl1H6l1CGl1Gyj6/FHbdu25amnnsTiKiXk8PqznhpsKsrElryVvv36cf/998ukujBEbGwrchyVQZJVZsIeYpOh1Qbm00GilDIDc4BJQCJwtVJKVmVrAF27dmXWrPswF6QSlLH7lwdUOAk9spFmzZry97/9TYazhGFatmxJgUPjqIDsMjMtWrSQP2oamE8HCdAfOKS1Pqy1LgcWAdMNrslvTZkyhUGDBhGS+i2qvPi0rwWn7YKyAv788MNypbowVLNmzQDILTOR67DQvEUjryMXgHw9SFoDx6vcT/G0nUYpdZtSaodSakdWlg/uFugllFLce++9mJUi+MS3P7c7S7Bm7GbMmDH0lI2DhMF+ChKHibxyM82bNze4Iv/n60FSXX/1FwP4Wus3tNZ9tdZ9T/2SidqJjY1l6tQpBOce+ulixaD0vSjt5sYbbzS4OiGgadOmQOWwVoFDExMTY3BF/s/XgyQFaFPlfhyQalAtAWPGjBngdhOUcxC0G1vuIQYNGiSnWAqvcCo4jhWZT7svGo6vB8nXQIJSKl4pFQxcBSw1uCa/FxcXx8VduhCUl4y5MANdXsKECROMLksIAEJCQggOCiLFs+pvkyZNDK7I//l0kGitXcA9wBogCfhAa73H2KoCw5DBgzEVZ2PJPYzJbKZfv35GlyQEUDmX1yQqkpTiyh6JBEnD8/lzNLXWK4GVRtcRaLp37w5AcNZ+LurUSbYwFV4lMiqKjKzsytuRkQZX4/98ukcijNOx48+rqXaWDYOEl4mM+rkXIkHS8CRIRK2EhYURFBQEyDpGwvucupZJKUVoaKjB1fg/CRJRa6eWhW/fvr2xhQhxhlNBEmYPkfXeGoHPz5EI4zz33HPk5ORIj0R4nVO9EOmNNA4JElFrdrtdJtmFVzoVIBbP8KtoWNLnE0L4nVN/4Cglb3GNQf6VhRB+JyQkBKhmvSTRICRIhBB+x2azAcjy8Y1EgkQI4XesVisA+iybsIn6JUEihPA7p4JEeiSNQ4JECOF3Tl0sKz2SxiFBIoTwO6eCRHokjUOCRAjhdywWuUSuMUmQCCH8jgRJ45IgEUL4nYiICABGjx5tcCWBQWJbCOF3oqOj+eSTT4iKijK6lIAgQSKE8EvR0dFGlxAwZGhLCCFEnUiQCCGEqBMJEiGEEHUiQSKEEKJOJEiEEELUiQSJEEKIOpEgEUIIUScq0FbHVEplAclG1+FHmgLZRhchRDXkd7N+tdNaN6vuCwEXJKJ+KaV2aK37Gl2HEGeS383GI0NbQggh6kSCRAghRJ1IkIi6esPoAoQ4C/ndbCQyRyKEEKJOpEcihBCiTiRIhBBC1IkEiagVpdREpdR+pdQhpdRso+sR4hSl1FylVKZSarfRtQQKCRJxwZRSZmAOMAlIBK5WSiUaW5UQP5kHTDS6iEAiQSJqoz9wSGt9WGtdDiwCphtckxAAaK03ArlG1xFIJEhEbbQGjle5n+JpE0IEIAkSURuqmjY5j1yIACVBImojBWhT5X4ckGpQLUIIg0mQiNr4GkhQSsUrpYKBq4ClBtckhDCIBIm4YFprF3APsAZIAj7QWu8xtiohKiml3gO2Ap2VUilKqZuNrsnfyRIpQggh6kR6JEIIIepEgkQIIUSdSJAIIYSoEwkSIYQQdSJBIoQQok4kSIQQQtSJBIkQXsqzyrIQXs9idAFCBCql1GNAttb6X577TwAZwK+ANKAnlcv0C+HV5IJEIQyilGoPfKK17q2UMgEHgQeABUA3rfURA8sTosakRyKEQbTWR5VSOUqpXkAL4FsgB9guISJ8iQSJEMZ6C7gBaAnM9bQVG1aNELUgk+1CGGsxldvC9qNyEUwhfI70SIQwkNa6XCn1BZCvta5Qqro9w4TwbjLZLoSBPJPsO4ErtNYHja5HiNqQoS0hDKKUSgQOAWslRIQvkx6JEEKIOpEeiRBCiDqRIBFCCFEnEiRCCCHqRIJECCFEnUiQCCGEqJP/B6RaHNNAUn52AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=rides[['yr', 'cnt']],x='yr',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    两年骑行量的分布差异比较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 每天骑行量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4oAAAHwCAYAAADgq5F2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOydeZgcV3nu3zPds2oZ7bakkSzL8m5sSd4xAYPBG9gGvGCEjcE2ueEazJabQIAEkpBAwiUJmCw3GNuIxGCz2ixeiB8IAYwXyTu2ZWRZGkmWta8zmumeun+cOnO+On1OnVO9d8/3ex496q7uqaruquo673m/RURRBIZhGIZhGIZhGIZRdDR6BxiGYRiGYRiGYZjmgoUiwzAMwzAMwzAMk4CFIsMwDMMwDMMwDJOAhSLDMAzDMAzDMAyTgIUiwzAMwzAMwzAMk4CFIsMwDMMwDMMwDJOAhSLDMAzjRQhxqxDir5tlPfG6IiHEkvjxvwohPl2l9S4UQuwTQuTi5z8XQlxfjXXH6/upEOKaaq0vw3b/WgixTQjxcr23zTAMw7Qe+UbvAMMwDMNUShRFfxTyPiHEOgDXR1H0s5R1rQcwuRr7JYT4DIAlURRdRdZ/QTXWnXE/FgD4GIDDoih6pd7btyGEOBvAN6MoGmj0vjAMwzClsKPIMAzDMDFCiHadQD0MwPZmEYkMwzBM88NCkWEYhilBCLFMCLFKCLFXCPFtAD3ktelCiB8JIbYKIXbGjwfi1y4XQjxqrOtjQogfWLbxlBDiIvK8Mw6NXOrYp/8jhNgshNgkhLjWeG08pFUIMSvep11CiB1CiF8KITqEECsBLARwdxxa+idCiEVxCOt1Qoj1AB4gy6hoPEII8ZAQYrcQ4odCiBnxts4WQgwa+7JOCPFGIcT5AP4MwDvi7T0evz4eyhrv16eEEC8JIV4RQnxDCNEfv6b24xohxPr4u/lkyjHrj/9+a7y+T8XrfyOA+wHMi/fjVsffXyKEeEwIsUcI8ft4/yGEmCeEuCv+Ll8QQrzP9r3bvo/4u/hjIcQT8Xf3bSFEjxBiEoCfkn3aF2/nNCHEI/E+bBFCfMn1eRmGYZjawkKRYRiGSSCE6ALwAwArAcwAcCeAS8lbOgDcAulSLQQwBOCm+LW7ABwuhDiWvP+qeF0m34hfU1wIYHMURY9Z9ul8AH8M4E0AjgTwxpSP8DEAgwBmAzgEUqxFURRdDWA9gIuiKJocRdHfkb95HYBjAZznWOe7AVwLYB6AAoAvp2wfkBu8B8DfAPh2vL2TLG97T/zv9QAWQ4a83mS85zUAjgZwDoA/N75bylcA9MfreV28z++Nw2wvALAp3o/3mH8ohDgN8nj8HwDTALwWwLr45dshv895AC4D8DdCiHPSP32CKwCcD+BwACcCeE8URfuNfZocRdEmAP8E4J+iKJoK4AgAd2TYDsMwDFNFWCgyDMMwJmcA6ATwj1EUjUZR9B0AD6sXoyjaHkXRd6MoOhBF0V4An4MUJoii6CCAbyMWgEKI4wEsAvAjy3a+CeBCIcTU+PnVsAtKQIqNW6IoeioWGZ9J2f9RAHMh8/FGoyj6ZRRFkeczfyaKov1RFA05Xl9Jtv1pAFeIuNhNhbwLwJeiKFobRdE+AJ8AcKXhZn42iqKhKIoeB/A4gBLBGe/LOwB8IoqivVEUrQPwfyG/0xCuA/D1KIruj6JoLIqijVEUPRvnNr4GwJ9GUTQci/ivZVgvAHw5iqJNURTtAHA3AKtjHDMKYIkQYlYURfuiKHoww3YYhmGYKsJCkWEYhjGZB2CjIa5eUg+EEH1CiH+Lwxv3APhvANOIcLoNwAohhIAUFHfEAjJB7CD9CsClQohpkA7Tf6Ts0wbb/lj4ewAvALhPCLFWCPHxtA8bsyHD6y9BCulZAev1MQ/Jz/ISZKG5Q8gyWqX0AOyFdmYB6LKsa37gfiwA8HvH/u2IJwTKWS8Qtv+K6wAcBeBZIcTDQoi3ZNgOwzAMU0VYKDIMwzAmmwHMj4WeYiF5/DHIUMjT4xDB18bLBQDELtAIgD8AsAJulxCQovIqAJcD+E0URRtT9mmBY38SxI7ax6IoWgzgIgAfJaGSLmfR5zia2x4FsA3AfgB96oVYLM/OsN5NkCG8dN0FAFs8f2eyLd4nc12u79NkA2Sop23/ZgghpjjWm/j8AA4N3B5g+W6iKFoTRdE7AcwB8AUA34nzGRmGYZg6w0KRYRiGMfkNpFi5UQiRF0K8HcBp5PUpkHmJu+KiLn9hWcc3IHPtClEU/U/Ktn4AYDmAD8V/4+IOAO8RQhwnhOhzbBMAIIR4ixBiSSx09wAoxv8AKcAWp2zHxVVk238J4DtRFBUBPA+gRwjxZiFEJ4BPAegmf7cFwCIhhOt+ezuAjwghDhdCTIbOaSxk2bl4X+4A8DkhxBQhxGEAPgoZ3hvCzQDeK4Q4Jy6AM18IcUwURRsA/BrA38ZFaE6EdP2U8/sYZPjwDCHEoQA+nGG3twCYqYr3AIAQ4iohxOwoisYA7IoXF61/zTAMw9QUFooMwzBMgiiKRgC8HbLIyk7I3Lfvkbf8I4BeSBfrQQD3WFazEsAJSHcTEecEfhey0Mn3Ut7303i7D0CGlT6QstojAfwMwD5I0fvPURT9PH7tbwF8SsiKqH+ctm8GKwHcChlG2QPgxni/dgP435B5exshHTZaBfXO+P/tQohVlvV+PV73fwN4EcAwgA9m2C/KB+PtrwXwPwD+M16/lyiKHgLwXgD/AGA3gF9Au5PvhMwz3QTg+wD+Ioqi++PXVkLmTa4DcB9kfmoQURQ9CymU18bHYx5k0ZunhRD7IAvbXBlF0XDoOhmGYZjqIfz5/QzDMAyTDSFEL4BXACyPomiN571/DuAo2pSeYRiGYZjG0q6NhRmGYZjG8n4ADweIxBmQoYxZqmgyDMMwDFNjWCgyDMMwVUUIsQ6ysM1bPe97H2Q46cooiv67DrvGMAzDMEwgHHrKMAzDMAzDMAzDJOBiNgzDMAzDMAzDMEwCFooMwzAMwzAMwzBMgrbNUZw1a1a0aNGiRu8GwzAMwzAMwzBMQ3j00Ue3RVE0u5y/bVuhuGjRIjzyyCON3g2GYRiGYRiGYZiGIIR4qdy/5dBThmEYhmEYhmEYJgELRYZhGIZhGIZhGCYBC0WGYRiGYRiGYRgmQdvmKDIMwzAMwzAMw1TC6OgoBgcHMTw83OhdSaWnpwcDAwPo7Oys2jpZKDIMwzAMwzAMw1gYHBzElClTsGjRIgghGr07VqIowvbt2zE4OIjDDz+8auvl0FOGYRiGYRiGYRgLw8PDmDlzZtOKRAAQQmDmzJlVdz1ZKDIMwzAMwzAMwzhoZpGoqMU+slBkGIZhGIZhGIZhErBQZBiGYRiGYRiGaRDFYrHRu2CFi9kwDMMwDMMwDMPUiE9/+tOYNWsWPvShDwEAPvnJT+KQQw7B97//fcydOxePPfYYnnnmmQbvZSnsKDIMwzAMwzAMw9SI6667DrfddhsAYGxsDN/61rcwf/58PPTQQ/jc5z7XlCIRYEeRYRiGYRiGYRimZixatAgzZ87E6tWrsWXLFixbtgwzZ87EaaedVtV2FtWGhSLDMAzDMAzDMEwNuf7663Hrrbfi5ZdfxrXXXgsAmDRpUoP3Kh0OPWUYhmEYhmEYhqkhb3vb23DPPffg4Ycfxnnnndfo3QmCHUWGYRiGYRiGYZga0tXVhde//vWYNm0acrlco3cnCBaKDMMwDMMwDMMwNWRsbAwPPvgg7rzzTgDA2WefjbPPPruxO+WBQ08ZhmEYhmEYhmFqxDPPPIMlS5bgnHPOwZFHHtno3QmGHUWGYRiGYZiJSrEAPH47sHolsHsj0D8fWHY1sHQF0NEa4XEM0+wcd9xxWLt2baN3IzMsFBmGYRiGYSYixQLwnfcAv7tbL9szCGz4LbDmXuCyW4EcDxUZZqLCoacMwzAMwzATkcdvT4pEyu/uBp74Vn33h2GYpoKFIsMwDMMwzERk9cr011d5XmcYpq1hocgwDMMwDDMR2b3R8/pgffaDYZimhIUiwzAMwzDMRKR/vuf1gfrsB8MwXu655x4cffTRWLJkCT7/+c/XZZucocwwDMMwDDMRWXa1LFzjYvnV9dsXhmkDCsUxfG/VRnz7kQ3YvGsIc6f14h2nLMClJw8g1yHKXm+xWMQNN9yA+++/HwMDAzj11FNx8cUX47jjjqvi3pfCQpFhGIZhGGYisnSFrG5qK2hz7EXASe+s/T5wew6mTSgUx/CB/1yNe55+eXzZpt3DePSlnXjg2Vdw04plyOfKC+Z86KGHsGTJEixevBgAcOWVV+KHP/xhzYUih54yDMMwDMNMRDpysgXGlHl62YzFwCVfBS6/rfZCTbXnuOsD0tlUrTnu+gBw5zXydYZpEb63amNCJFLuefplfG+1Jyc4hY0bN2LBggXjzwcGBrBxY/nrC4WFIsMwDMMwzEQllwcQ6edn3gAsu6o+bh6352DaiG8/siH19TseTn89jSiKSpYJUX4oaygsFBmGYRiGYSYqUQQc2KGfjw7Vb9vcnoNpIzbvSr92NnleT2NgYAAbNmihOTg4iHnz5qX8RXVgocgwDMMwDDNRGT0AFA+S58P12za352DaiLnTelNfn+d5PY1TTz0Va9aswYsvvoiRkRF861vfwsUXX1z2+kJhocgwDMMwDDNRGdqZfD56oH7b5vYcTBvxjlMWpL5+xanpr6eRz+dx00034bzzzsOxxx6LK664Ascff3zZ6wvebs23wDAMwzAMwzQnNOwUAAp1dBS5PQfTRlx68gAeePYVa0Gb848/FJcur2zi48ILL8SFF15Y0TqywkKRYRiGYRhmolKpo1hJe4tmaM/BMFUi1yFw04pl+N7qjbjj4Q3YtGsI86b14opTF+DS5ZX1UWwULBQZhmEYhmEmKkOGo5glR1G1t6BCT7W4WHOvbL2RSxlqqvYcXzsH2PyYXDbjCOAPPipFIvdRZFqMfK4DV5yyAFd4wlBbBRaKDNOqcJNihmEYplIqcRRD2lssuyp9Hbk80DdTPz/rQ/6/YRimLrBQZJhWpNJZXIZhGIYBKstRDGlvESL6RvaT7R90v49hmLrCVU8ZphXhJsUMwzBMNShxFDP0eqtWe4tRKhTrWEyHYZhUWCgyTCvCTYoZSrEgj/nN5wJfOl7+v2olMFZs9J4xDNPsVCIUq9Xegh1FhmlKWCgyTCvCTYoZhQpDvusDMvRYhSDf9QHgzmvk6wzDMC7M0NMsQnGZp31FaHuLEZIXyY4iw5Rw7bXXYs6cOTjhhBPqul0WigzTinCTYkbBYcgMw1SC6SgWMgjFpSuAw15tfy1LewvqKBbZUWRamBpF+LznPe/BPffcU6WdDIerXTBMK8JNihlFtYpJMAzT2pRbCbuS9hgdOeCINwIv/Vov650BnPtX4e0tosjIUWShyLQoNSw0+NrXvhbr1q2rxl5mgh1FhmlFlq6Qs7U2uEnxxILDkBmGqSQEvZL2GACw8dHk8xmL5eRUaJumwjAQjSWfM0wr0oYRPiwUGaYVUU2Kz/lzvaxrCnDJV4HLb+M+ihMJDkNm6gEXTGpuyh2gRpEl9DRQqBULwKPfkE4JZY9n8spkxBCm7CgyrUobFhrk0FOGaVVyeeDIc4H/+kv5fM4xHGI4EeEwZKbWcN/W5qfcEPSDe4Exw20sDANjY0BHipdgOycUe18GRg8Cnd3e3QYAjOwr3X4I5YbaMkytaMMIH3YUGaaVobkkPLM/MeEwZKbWtGE4VdtR7gDVzE9U+MRa2jmBCHj439P/nmKGuoY4ilztmWlG2jDCh4Uiw7Qy9GZOczyYiUNHDnj71wAIvWzWMfUNQ+awxPamDcOp2o5yB6hm2KnC1yLDd048dnv66xRa8RQIcxR58oJpRqrVLsbCO9/5Tpx55pl47rnnMDAwgJtvvrnsdWWBY0UYppVJCEUelE9YRvYBiPTzN/wZcNwl9dk2hyW2P20YTtV2lBuCbvZQVPhaZPjOib2b01+nlAjFEf/fNFO1Zw6BZRRLV8j7nm0So8IIn9tvzzD5UkXYUWSYVobO+kaR+31Me3Nge/K5WRyilvDMfvvThuFUbUe5IejUUeybpR/7HEXfOdE9Jf11SjmOYrNMXnAILENRhQYv+Sqw4Axg6oD8v4ULDbJQZJhWpsA5igxKheLofvv7agGHJbY/NQynYqqEGqCe+j69LN/rH6BSoTh1nn7sE4q+c2L2UemvU8rJUWyWyQueKGNMcnnpZl93L/DRp+X/WdrFNBksFBmmleHQUwZorKPYLDP7TO3ggkmtQS4PLDxDP++e4h+g0tDTqUR8+YTi0hXAEee4X++Zlv73lHKqnjbL5AVPlDFtTk2FohDiI0KIp4UQTwkhbhdC9AghZggh7hdCrIn/n07e/wkhxAtCiOeEEOeR5ScLIZ6MX/uyEELYt8gwE4xRLmbDwOIo1lEoNsvMPlM7lFs1baFe1r+wpcOp2pbiqH48tCM9JaFYANb/Wj/f+Kh+7MtR7MgBZ31YP++eCgycDnTE+ciZchTLcBSXrgCOeYv9tXpOXvBE2YQhaoH0nlrsY82EohBiPoAbAZwSRdEJAHIArgTwcQD/FUXRkQD+K34OIcRx8evHAzgfwD8LIdTd518A/CGAI+N/59dqvxmmpaA3cw49nbiUOIr77O+rBc0ys8/Ullxe9tZTLHtXS4dTtS1FUghmrOD+LVC5dWt/rpftf0U/PhjwG7LvZf34wr8Hrr9Ph69WVMwmwFHsyAFv/ZfS5a/9k/pOXvBE2YSgp6cH27dvb2qxGEURtm/fjp6enqqut9al6PIAeoUQowD6AGwC8AkAZ8ev3wbg5wD+FMAlAL4VRdFBAC8KIV4AcJoQYh2AqVEU/QYAhBDfAPBWAD+t8b4zTPMzyqGnDEorF9Yz9LSGVd6YJiKKgP1b9fN6utZMOGOjyecHdtgLy6T2QQTw+/8CjnU4dordG/RjFbY6dT6waz2wZ5M8Z0ICwMyc6hBHEbCfg7/9V+DFX9Sv6mi51WaZlmJgYACDg4PYunWr/80NpKenBwMD1Z2cqJlQjKJooxDiiwDWAxgCcF8URfcJIQ6Jomhz/J7NQog58Z/MB/AgWcVgvGw0fmwuZximwFVPGTQ29FSFJd50CrDzRbmsbxbwps9KkciOU3twcA9QJAN4Xw4b0xiKhlAc2gFMP6z0fb7cOuo0uqBhl8pZmzJX/j96ABjeDfQG5CqajmIxUCgO7y5ddnCPFG71as/DE2UTgs7OThx++OGN3o2GUMvQ0+mQLuHhAOYBmCSESGtqY5t2ilKW27b5h0KIR4QQjzS76meYqkBnXjn0dOJSEnpax6qngByIzSJVDg85nsMS243925LP2VFsTopGD0Ja1ZTiy61z9Vek7CHrmDpfhrMO7dLLbnuLLObiuzeZERDFkWSYswubUKTUo+qomihbQgr7TDuM83eZtqGWxWzeCODFKIq2RlE0CuB7AF4NYIsQYi4AxP+roPhBAAvI3w9AhqoOxo/N5SVEUfT/oig6JYqiU2bPnl3VD8MwTUmijyIXs5mwNFooAkjM3+3b0oDtMzVl3yvJ5+woNiemUHQJPm8fxKn+balCLZPmACIX5zw+oF9/+cmwfoK2dj4hrqJPKAL1qTqaywOzjtbPl7+bJ8qYtqGWQnE9gDOEEH1xldJzAPwOwF0Aronfcw2AH8aP7wJwpRCiWwhxOGTRmofiMNW9Qogz4vW8m/wNw0xsuD0GAzQ29FRBXYM9GQpZMK3B/jYWisWCFBQ3nwt86Xj5f4gT1oyUhJ7GjqL5GU3hbzL3RP+2lFDsn19ZP0HbxFZIQZsQoVivqqP0e2+na4OZ8NQyR/G3QojvAFgFoABgNYD/B2AygDuEENdBisnL4/c/LYS4A8Az8ftviKLxke/7AdwKoBeyiA0XsmHai2JB3mhXr5QhQf3zw5LxR7nqKQNLMZsGOIp0ouLgblk1sXty/feDqQ37jXSOdgk9VdU/qcjZM1i/HLdqYxOKts/oY+aR6a+P7AeG4zDTqfPD+gkuc2Qf2YpvhRS0Gd7lf0+9qo7SIkJZr41y7/8MUwdq+usXRdFfAPgLY/FBSHfR9v7PAficZfkjAE6o+g4yTDNQyUCF3kw59HRiUhiRBRwojXYUAVkev9sz2GRah32mUGwT1yTECXMJnGbEFnrqq3Ca7wX6Zsp/Lz8er8cj1BKFbBYAmx7zvD/F2bO18AhyFPf431OvqqM0tDbL72+7TVQwbUctQ08ZhgmhkpCdAucoTniGLDlI9WyPoTCF4h5rKjnTqrSroxjihLUStqqnvs+Y7wE+/ASwgtxrfMeXtsbon19ZP0HbtgojpctMfKGn9aw6OlZm6Gkl93+GqQMsFBmm0VQyUKF9FDn0dGJi5icCjQ89BVgothvtmqPoq/5pOmHNns9ocxS3PZ/+N8M7ZcGZjk69bNTj6JkVT5d5nLs0Z68aOYpHnqsfzzq6/lVHqUDP8vvbbhMVTNvBfjbDNJqsAxUKO4qMTSjaqgjWmpLQUxaKbUVJe4w2EYr982Won/N14oS1Qpig6SgOPhyWy/e7u4EjSFaQ11GkoacDwPyTy+8naBWKGaueLvoDYM198vEbPwMcc6H/76tJuY5iJfd/hqkD7CgyTKOpKGSHq55OeGxCcawQFrpVTcaM8ve+yqfN7swwSUraY7RJ6GkWJ6wVwgRNRzFEJCoeux3jratdjp66bh+5WS9b/1v5/2W3SievZ7p+7S3/KJ29KHJf75U6iqID6CHtPBpxL0zkKGYQipXc/xmmDrCjyDCNZtnVckbaRVrITqI9BjuKExIqFLv7ZcVRQLqK+a767UeW0NNqOzNcNbD2tKujuHRFuBNWSWVPdY6u+gawbY10oHKdsrro8ndX71wdG/W/x8WejUBnr5wEsB1fV/XU+z8FDP5WXrfLrgI2Pgo88nX52hGvlyLRdb0//1P7PmdxFHv6gQ7ye9GIyaaEo5ghoqOS+z/D1AEWigzTaLIMVEwKnKM44TmwUz+etgDYEg+eRvYDvdPtf1MLxoyJirTQ02pWmmyFcMBWZ3RYT0AoCsPymHe0eGBSR06eI186Vudhzj4OePUN8reXirdywwTT2lMMPiT/VetcNUNPs9A/EItEh1AMvW6nzNPL92wGXvyl+++e/bF9eYijqKo99/QDghwn26RprSeTfH0UXds/8R3A8/cAz/6o9G/qWYyHYRy0+C88w7QBaqAyY7FeNndZWDJ+ouBAJGdumYkFdRT7F+jH9a58WuIopoSeVqOAgwqBu+mU5g8HbHXMiqeKQpu4irl8MnT6gs9LwWP+9pYbJuhrTwFU71w1Q0+zsPxqoLNPPrYJtdDrdupcvWzPRv/fKdS2Xds3STiKKUJRCfW7PiAnkNRE0l0fkEV8ikbYfDmMpYSepm3/u9cCF/x98v0zj6x/MR6GccBCkWGagVwe6J2hn19+i32gYmIO1Dj8dOKghNKTd+hlNG+s3gVtzBzFfS8DX3ujPe+w0gIOdOC188X093LVwMpxCcV2CT8dG0vm8pnnsqLcyp6hQqka52q5QlG5V5098rktBzX0up1KHMW9m/1/p+ibqR+HfA4lFLunyjxFhfl7U4/cUnrOmDmXvu2vujW57OyPh93/GaYOsFBkmGaB3mhCbpJjY6Xv4/DTiQEVStRRfPEX+nG9HUXbuTf4sH3WvtICDiEOjYKrBlYOFYp0QN4uBW0O7k5Osrl+R5eukILKRlqYYKhQqsa5Ghp62jtdtrVYcEbSversla/b2mOEXrdm6Knv7+g+KXyO4uiwfk+Jo2gcv3q0oEgLPfVt/6nvJZ/zfZxpIjhxg2GaBXpzCEnkt91IufLpxCBEKNW7l6LKF7Jh5h1WWsAh1KEBuGpgpRQLwDM/1M878nqCqhUdRVuu2FHnJ9/jchRVmsAtF8i8QsW0helhgr4WHOPvq8K5miYU+2YDM4+Q15eZf6nIK6FoTAIUC8DMJWHXLQ093bvJf72P7x9xFH33QPp70zMtmaNY7QiGEGgxm8JQMn/Xt32zorDr/GOYBsBCkWGahYSjGDArbBWKHHo6IQgRSvUOPT24N/11WhGykgJOQLhDA3DVwEqwFWGhUQy+Y26uq9GVadMKH1HSBuq5fNJVBYBd62XLh+kL7Z8pVChV41x1RaN0dAIfeUqHlrpQjiK9v6QV41HQ67Z7KtA1GRjZJ6sfL10BPHqLrIZqsuA0YEMsuvtI+oV5fzPPH/pen6PoE+q5LikuKzkPzTzHwhDQNSls+509yWJRLBSZJoJDTxmmWUgIxQBH0TabzyErE4MQoVT30FPP4IbO2nfkgEtvATonJd8zaVZYAYfQUDauGlgZPufaVqnRRj2KiYQQGrLsO5d3ritdtm+z+zOlhawqqnWuuiYZDz3BLxKBpFBUlYx939vSq5LXrRDAlNhV3LNZLj/kVaV/d8EXgNP+l35O8/Spo2g7f15+Qr/ePSXdUfTllu5cW/l5aLb4oPdn3/YnzTbWxUKRaR5YKDJMsxBVI/SUHcUJQYhQqnf+mOmymJhhdXs2lLqe+7cBQwENwn0Dr75ZXDWwGvic62d/GraeahQTUcWbbA3bQwkNWU5b5+iQLNSUhvmZVMjqoSfpZblu/fj0P6reuepyFLsmh31XSigC+h7j+962v1C67yr8dO9m4NHbgCduL/27ucuS9zGXo+gTqttfSP7+mPfBEKHuOg9DzztToNPf36UrgEV/YN/usReVOqA84cs0ESwUGaZZyFrMhoXixMUnlID65yjmu9NfN8PqBkkYGm3rcdOpfhHgG/id/kftXzWwGsLJh8+5dlVDNam0mEi1HMnQkOU0R2fX+rB1mJ8plwe64vYPXVOAd3xTvzbtsPLOVds5MLTD/t51vwz7rvJEKCpXrJwcP1XQZmwUuPtG++TntueSv1OuHEXf+bNpdbKfp3kNKKHeMy19PeYxSzvv/v4I4EvHkevOEIo0oqMjBxx9Yen23vDncr92bUguZ0eRaSJYKDJMs0BvbiFC0VaVjmciJwYhM+T1FooiZaBLw+rU4Pa+T+nX6cBoaLtfBKiB3xIa4UoAACAASURBVFzi0Mw5Xj8eDnAlW5l6hXL6nGtapTKNSouJVKu9QWjIctpAfedLYeuwfSZVobhvRrJv7o61YeukuM4BWgXZJOS7SjiKsVAsp0oxbZGRtj/0d8oVeuo7f4Z2JX9/bEXdzF6ZNsxjlnbeDe2UPSLVdbff+N7NiI6Nj5SuY86xssXHyL7kchaKTBPBQpFhmgV6cwgKPbXkKLaqo1gPd6SdUEJpyqHyuegABk4H3vBp/Z56h56qc2/mEgBCPu6emgwBpYNbGr63d7N9nWkD21weOBgPsKbMA979A/1aSPgqpdXOv3r0hQP8zvWC08LWU2k7lGq1Nwhx4gGPUFwXtg7bZxoXijNlpVQVLlmOUMzSIobi+646LY5iOf0jQ4Ti4COGo+gQir7zZ/IhSUfWdd129qWvxzxmWaorm3UF1O+v+m2xHattzwO7LBMPXL2caSJYKDJMs5A19NTmKLbiDaZZCl00knKESvGgLqu++Gzg+vuSxTDq7SiqfZ0yVwvYaYclQ0DLGdy6Brajw8DOF+XjOcckw8qGdoav33f+jQ43l4gsFoBffin9PdXoCwdI5/qYt7hfP/SEsPWU26heUa32BiFOPOAJPQ10FM3PNFbU52XfTCDfpUOuyxGKWUQMxfdd2YRi1v6RxQKw9Tn/vhzck5zQSoSekvub7/w5/hK/owgAMw5PX495zLJUVzYZHUr+ttju6VufBVZ/s3R5aC9MhqkD3B6DYZqFavRRbFYHJI0Qd0S1VWhH0kr2r7lXOoe5fGl5+J6p2sWbu1T+30VmzOvuKMbnXkdeDvj2bgYObEu+p5zBrRrYmp+/d5r+/LOPlQNvVZI/i1D0nX9fPw/Y/JheZjs29UKdKzs9wqLcvnC2FhbHvV1XN833ypDJV56Wz31N0RVLVwDP32OvkhpS7dPXXiC0/2BHDjjv86XHu29mMmQz7XeUOopHXwA8ZynoY/tMw7v1+aoE0YzFUnjuWi/FQa4z7HMA5YsY33eVJ5VRlVBUEQw//N/AE9+Wy2YdDZx1Y2k/xpBWGoqxoswvVNz9If2Ynltp7XQA4MQrkxEKruOnJrBs2I5ZaP9LG6MH/BNjz9xlb2P05J3Aaz5a398WhnHAjiLDNAuJHMUJ1EexWmFlrUqIULa5Xq88o983vFueP7TdRN0dxdiF6cjpgfCB7UAU6feUM7jtH7B//i1P6ffMPlr+r3LmsghF3/lHRSIla4hnNcJbQx3Zchq3u5zVe/9Uv2f51cDb/kU/t7XosdGRAy78Yuny8z8fVu2zUkeSQs8bhZnXF5KjOGk2cMU3gYFT9GvzT3FX26XboEIRkJMsoUVyFKH5lia+74qGZ9L0hlxefj7FeX9jLxiVKWogAtb/Rj+l1xq9vymheuKV9tX0TjMcRcd9kEbhzDpaPz7urfZjFhqqbGPkgP+3xdXrdtvz1QsfZ5gKYaHIMM1CNfootmLoabXCylqVEKHsG3w9crMMkxQd0tED6isUo0gPzgQRisWRZKGGcga3y68O6Of3Yym4euPw0yxCsZLwstBJjGqFV4c6suU0bg8Z4PfNSgqJUKEI2IXQoSeGVftcugJY/Hr7a1n7D25+3P8em1AsFoBHv6HdVOUYzT5Gv+fK/3RX200IxXhCo5KCNuWImJDvqtPiKCro9+I6buWGxJqY50suD8w/2fJGIXOh06qeKlSURb4XuPr7enm+2/55QkOVXduqx28Lw9QYFooM0yxkLmZjcxSj0mXNTqWFLlqdEKEcMvhSDpdyFesZekpn8KmjCMjeiIqsg1s1sPV9/jX3SsHVQ4Ri6LVQrjMDhE9iVKv4TMjAs9zG7SHn2KSZRg5bhnPMJoS2BeSxAfKcsn2m0/4wvP+gcnQf/Gf/e02hqIT+3R/U5/rIfin01/2Pfp+twJjC5ihOX6SXfff6bC6zT8TMPQlYcAYwdUD+H9pXNG0iIEQoViKOKHs2lS6zCXglEkNyFNXn6eyVxXbU78WWZ+zvV04mDccNZXSoPr8tDFNjWCgyTLOQcBQDQk9ts/mtmKNYzbCyViREKIcOvlatBLpioThSI6FoDaH8hn69IwdMmqWfHyB93dIGt7SX4swjkwPbkM//u7u1e1k8GC5iKgkvy3WFXXPVCq/2Vn+cW37j9pDvuG9m+Y6iTShufT7877c8Wbqse2q4SFSObkjrFPOYpgl9mrOYNsFnCsViAXj43/Wy4V3ZXGYlYmhe49xl+vGMI4Dr7gU++rT8P6SvaLEAbHhIP3/gr5LClQqwDkf+XCXiiGL7Ls1ehYAU56tWYrzSMpDiKCqh2AcIARwSt9TZ+qz7npvLp+c2AvaQ2NEDlf22tPsEKdMysFBkmGZgbAwAcUBCQk9tN9JWDD3NWlGv3QgRyqGDr92DuqCNK/+lElwhlD/6sH6PMBxFOkBWg9sZR6g3y7Yel3wVOPev9fvOujE5sA3+/ETshIafpp1/tE+jjZ1rwwb11Qqv9p0rR59XnkgEwr7jvlmNcRQBHTIqchgXBZtWhf1t1mq7pnMVGk6ZJpxNofj47cDan9vfG+oyi46kwHnbv+rHuS7/31PUtf3IzXrZjrVJ4ZpwFB1CsRJxRLGtv2CpHFockft4P2kN5MxRjM9X9RuphOLYKLD9Bfe+dE9JeW2qzLW1bWvpCuCo891/m0a7T5AyLQMLRYZpBkyBZ7shmrRLH0UlHmh7g+5+f6hUq/W+c7F0hayeaEMJ5dDBV/+AdnxqkaMYMuDuyCX7oZmVT3N5XSp+1lGyrceyq4Cefv2e4T3Jvwn9/HSgHioU1fk3+1i9bOp8ef5d9zPgmDen/33IoL5a4dVLVwALz3S/TnPeshLyHU+aZa+KGYISit39eqIgxFFUuYHrfiWf57tkxV9ACq2vvcl/3WfNmzOFYqijn1YF1hSK1XCZze0dJNdNlgqqQFh4NP2OXb/LaRMvqthUCF2TSpfRCqkmL/5CP3aGnsZCUU120PzSWy5030PUpGzfLB3Om+uWy6Ycar/vjg7J7+h1H9fLROCQe9ph7T9ByrQMLBQZphkoyYkJKWbTJu0xgLgMOHFUuyenh0q1U+/Fjhxw9qeSy+YcnxTKoUUVll9d29DTkAG3yMkBlcKsKFks6PwjKqDU4B8ADu5N/k3o56cD0SwFbXL5pLg98k3y/Mt3ARdYqnWa+Ab11QivLhaAx/4T2LFOL+uaDBz6Kv1c9dUsh5DvuG+WDNlTkxHlCMUZi7Sjs2cQ+Pdz3EIvkRtY1Nsc3i0fR2PA4EP+6z5r3pwpNEId7VShSEKw+2ZWx2U2v/+EUMzoKIYIV3qfEo7fZjXxcslXS3Mk3/jZ8P2xCV1XBWKTMZejSEJPiwXg6e/p14Z2uO8h6rhOmqXDeacfJpcVR+whsWqibg85zt1TS99nY/Hryo8MYJgqw0KRYZqBEqFYrqPYokIxioCDpDrmno2lYoFSreIgzcJeY9B4/t8mhbIafB12lnsdyn1UQnF0f/WLG4UMuFUfRcV+w1Hc97I+T6mT1k0cxYOGozju+h2DVA7/A/04i1AEkoPufVvd+2LDN6ivNLyaTozs26yXj+wDpszTzysRiuo7Pvm9yeUqPA9Ci2nlyISGnh7YoXMD928FXib5hhsfcQu9LCGjadd91rw5U7QGO9qBQrF3enVcZvP7p7+ZWYViiHAdC8hRBOTEy7KrSnMkl13lvg7yPcCC04Hph8vnNtFtRhq4CHEUH789WYiIYp5LKsIn362Xqe+3OGrPb1S/J7R66+RD/PsOuIUuwzQAFooM0wyUhLqECEVbjmKL3mAKB0tv7mk5I+3We1H1ZlPYnJpcPlklcd7J9oqGyu2JxsKq52YhZMDd0eHOUQSSoooWsKF5QDZxlssDi17j3u6xFyXzgbIKRfpd7SeCy9x/G75BvRJhahAMyM8eWokyTTCtuVcP2mnT8XLI5YGFZySXqUFr73S9n1kcxWIB+A2pNGqrZgnYhV7WkFHXdZ81b86cuEvNY12qH4eEnvb0S7esGi6z+f1TIZW1WXuIcA3JUUwjzW38s03Adffpa8n229U9OWw7Vnd6VO9/Z1+2e4g6rjTsWjmeLkfRJhRfdXn6NhVpfTwZps6UcaUzDFN1zBtbuX0UW3Um0pZPt20NMG9Z6XKg/Xov7jKFosOpoU7zu7+fzOtT0Nyekf3JvmiVsuxqGZ6VRkkxmx3J1xNCkQgsGnrqcg7ozP285dJB6x+Qg+qT3gmsf1C/nlkokkH+voxCMWRQn8snB9evukw6LCH4BrUq92nvlrD1pWEOUlXBGVrJdtxR9AhF5YSGuoKrVia/k6who67rfukKKahD98P8DjpywCX/ov8+3yMF4vKrpbP0vffJ5SFCUV0bafsUWsSrmo6i79pefnWyjUQ5QhHQbqPr3Feune27nH00sHdz6XITm6NIv6vO3mz3ECVaE46i2s8Re8izKiZGheIZ7wdeftx+zI+6AHj+p/IxC0WmiWBHkWGaAfPGEOQo2vootqpQtISZbkspdNFuvRdpiX3APQCnQsk1EEy0L6hyQZuQPLaOvMztU/k4ZjGb3Rv046nkOOZ79ODTFXZMr5N33Vla+r/cHEXAcBRJ6KlPKGapzEtbM2TJIQ0VTPuqIBTNMDr1m9JXhlDMWm1006pkvmLWkFHXda+crGlxXpnokE7WqdfZ328bqO8hwuGUa/V5RydmsghFtU8LX63fM295uMsMVDdHMSQ8OtEeo0bDR+XaRWOlAmzmEvffLXmTfmxzFOl31dkbfg+JIn1cc1QoZnQUJ82RjqjLUb2UtEphocg0ESwUGaYZKCdH0TZIa9UcRZej6KLdei8GO4pkQNLhqGrYRYRitQvaqMEt7Ss2aU6ySIUa4Kp8tpLQUyJ66MBeCC0uXXmBic9vcTQqEopkkD96QOfM0v1/zUf1997Zl21QH0XJfcoi4n2DWiVWhnel58mF4BqkTiIucWjoadbQUdXqQOUrZg0ZTbvuc3ktemceKYXeCY5QQFvOGW3vQUOIqcvk+u6LBT1JQN32XB6Ye6J+funXwvodjm8vzVHMWPWUhoX2kOvoLf+oz/FKQ09DoN+nKbzp9ucuTwqti7+sX/M6in3h95DiKMYLrVlzFEfs14zanhKK0xbGf+fI36QilIUi00SwUGSYZqCcqqe22etWrXpqE4rP/thdrrzdei/uXJ987nQU1QSCsA8miwVg++/18+9cW/2WIbl80vE++vzksVDVEJUDlZajONUQQCpP0eUo0gkUm2PSS1qsVOIoAjpPka7npHcChxwnH3f2ZhvUj+xPXudZRLxvUDtecAaVu4quQSoVOKHFbLKGjipUvuLSFcDAqWF/47vuo0iHFE+eI6+VXzoq2r74i1I3a8eL+jFtQ5InfSVdjiI9h+j3CBgRABkndkqEYgWOIqBFzKsu1csWn63P8XoIRSqYzGsyEVFwhyG0yOe1pWCYjmLoPYQeU5qjqERjVLRHAI0cAIZ2AQfjCr1KKLqg32erRgYxbQkLRYZpBsrqo9hGoac2YTA26i5Xrma/T39/8m/e9JfhDk+zQAcTCp9QzHVJBy7xWpwP9txP9LJXnq5Ny5Ahknc4Vkyed+OOYjwgHtqZ3LYSipPmlOZPqjxFV44iHSjaHJPOXj1wH9pV+noa5vWkKp+a/e+mDujlWdpDmMI1S59L36D2MFLkp2ZCkYaexuJmzFHxUfU4Hc54DCirVspz6fDXkX2YDQycDiy9Sudldk8Jc3aHd+sJuMlzZFjsCz+zv3f3YGlhHeooziCOIj2HXUKRnkNmL0EaAZB2Ptn6xq65P/ke+jvqijgIYSqppEvbO4S0x6iUNEfRvAdQaI9Cq6NIhWJf0kFV1zQg3fm9W2QrmrFiUqzaitkA9uiA0aFkfqJXKHYAiH/T2VFkmggWigzTDJQUswkJPbUJxTZyFCm2ioi5PDBvaXLZ4a9tLZEIlIadAm5nQQ0gbG5BvVqGmE7YWMHeiJsWP6EiSeUo2vLJxkNPXY6iJ/QU0APxLI7iWLE0z0g5iuODfCEdSxoG6qrgacPcnyzukRrU0gqbA6dpgURDgWslFG3FbIBScUNbeYzsQ9moCQXaSuPGR4Hr7wPe+lV9nGcdFebs0rzTSXOyV07eGTuKIpes1kvFg0vomZMNlE6j+JQNV9/YVbcl35eoelqJUCTXJj3HqVNXs9DTFOGd5mjS42/NUTSK2QDyHnLilUnhP7I/2ZuTHhNb6Kn6G9v2sghFQH8mFopME8FCkWGagWr1UWynqqcmttL35uA7pEJls2G2xgACHEXLIK1eLUPMqoNjBbvTQBvYH9guB7sP/bt2mXa+WBoWq4Ti6H67A6o+f0dnqaOqUALCrLaahq0U/z5DKKr2EC63xYfprmVxFAF5zNVn68jLVgJKIFGhuLfCFhku59lWzAYoPVdDC9iYzpqJmkjY/Lj8f8biZJVf5RyH5mTSSraTZ2evnKwcxf4BWaxpfD+osHGkDKQKRfpdOiYPQr/TSkNPFSGOYq0m5PJpoae0mJchhKnD6XUUyXf++O3Aul/a9+V3dwNPf5fsG3UUqVC0HLeRfcC9n9DPt7/gTwFgocg0ISwUGaYZKKl6GtIeo41CT0OcB1vp+xKhmDEvrZGoULJ7P1n6mq89hm0QWK+WIaaLNlYwqiEaoaeAdLm+8x7gJ3+slw3tLA2L9fVSHHdUU9ySchxFW8igcqCU4FTCl7otWXLwKnEUFeo66ZqcFMq0kXfNHEVLMRug9HP4Jiy64lBRWgDJxvKrpehVvSHnnpR8fbyNQmD4L+2NOfmQbJWTi6PArtgJp/mJgCEULftSLMh8a8Wvv5KcIEm0s3GcE6FFgSppj0FJCEXqKNajmE2ao0h/ZypxFMl37vtun/oB2TeHo2gLPR0rJB3FB//ZnwIwLhRbNDKIaUtYKDJMM1CtYjYtG3oaIBRtoYqt6ijSULLd60tfdw0Y1SDDNgisV8uQEkexaB/AUQfqmR+GhcXSXoq28FMVepqWf6UK2hSGwnMIrY5iLLjGhWIslBKhpxnEdyU5igpVibV7anJ5VUNPLTmHQLij6BPPPf26j54r7/KYN8tz6jby+thY8jzrrMBRnDQnQ9XLAvCrr+jf1i1PJ4Ue/S7M80hd5zTse/ua5ARJSDub0AmJSqqeUqhQpNu2TQhVGyrGzMiaNKGacBQDitkofN/tPuLQOx3FwGvZlwKgWo6wo8g0ESwUGaYZMENGbQUiTNqpmE3IjdZW+r5VhaIvlGy7ozXIuKNoGQTWq2WIGW5ZHE0O4EVODoBffkIve9TIpTJRYbFUANkcxbTPr0i0yAgspmK7lva9Ij+bKjSkhKJrEO3D3JeyhGIsBLonJ5dPmo3xQhh765CjmE8RiqETFirv8vWf0q9NPhS46MsABHD3jcleqr/7YdKRUYP2tN6FFDP0NK1A0KTZsuqlEnoPEPdz/ytJoZdoj5EhDFeJhpB2NqH9JKvlKHb2Ar2xg54IPU1x9KpFajGb+L4ocqWh55kcRfKd+77bxHlPvlP6G5SlgnFaCgCHnjJNCAtFhmkGsoae0ibAifW0qKN40OMoukrfm0JxKENeWiPxhTvZ8haBZI6eSb1ahuyx5CiaTvZ33gM89P/088gz8FFhsYnQU0clXMAtFIuF5P79x+Vh7UFs19v+rck8RxV6OmUexkVZvYrZKEaUUJxivCB0COPvH3C3lQnBFRr33D12F838HFkmLHJ54NUfxPj3eegJsnrlsz+y/y11ZNQ+hArF/YajSKtequbnqnLm7GPl6yFCL5/iKIbkDdMwSNc5EdpPkkZmVOIoAlpAuUJP61L11NEew/bZfFVPRyzFbAD/d3vEG8i+WdpjAIYT7MidVqSlAHDoKdOEsFBkmGYgazEb1+CoHUJPqcPQNyu99H2rOoo+J8qVd6Vm1G1ugRr4vvqDetm0w7I1hQ9hryVHkQ5sXn4yrPAGRblMNPTU1iJDiRibUFbuz+9Jy4MtT4a1B3E5inTiQTks+S7ZXgGorJhNYTjbgDCKtHjuIo6i+tzqGkprKxOC67fnxx8h4ZIpjmLWCYvOHmBaXEV0++/DizKpgfrokPxufOyjVU9ny//N5udKtKnf0ZB96ejQ16N53YbkDXcF9FFcukJWdM5CpUJR9Tjd/4pu1zR+nxI6TLLahFQ9tbmZQmixaHUUjfYYCt/5uvDVZN9cVU8P2JfbSEsBYKHINCEsFBmmGcjqKCYGZ2QGsx1CT0//Q/14/snppe/NypatIhR94U6usC6fo5bLA0edr5+fcm22pvAhlDiKxeQExebV2depXKZuX45iSuhpJe1BnI6io1qlCj/NUiDIVlwnS/jp6JC+vqmjWO22KFufdb+m1pdWzEZNWJxAmrbPPiZ9wmLGEfL/XeuBXZ7vVH3n44IiCgvVV7mbvdOTIYTmvgP69zi0QJTaF5UvGdpHsn8g+V26Qhg7csDxb08umzQbmLfMve5KQk+BZIi1mhxSAqZWYadAehXZNKEIaJfTmqPocBTV+XrxTVpodpHenGNk4sTVR5Fex2oSwkVaCoB5/jFME8BCkWGaAfPGMDaaPktOb6C0al7LtscgjmLXFKA7LoPvG2i1atVTX7hTSWhhTEiOHnXbajHg8LXHGNqdbX3UZUoIRct60oRyJe1BbI7iyL6kEEwIxVjoD+8KF3s2oZgl/JQKZ5qjWO22KGlCUa2PDrRf+FmyCfyqldLdoULjslvSJyxmxkIxKiarq9pQjkyiiExA0SJVxXbSHPd7zIF6aL4lzZfM0kdy+dVhxWwAYPNjyecLz0wXJdUUiir81CfUqkFIjqJr++r4ZWmPAcgJtuVX64q2fdP1+Urvtc6qp+Q6nn6Yfd8AfwoA5ygyTUgNr3aGYYKxzYAWR5I3pvHlBekiKOiNrGVDT+MBUuckGdLU2y+FQlqLg7EiMGyIiUY5iuqYrF4pXYj++VIMLl1hHxwvXQGsudftBLkGQmmhpwraY7HaA46xYmmfvrFCcoKid5pd5NmYvjjpMvlyFNNCTytpD0KvIdGhr8effUYvV0KxWEjm1H79fOC0P3Qfa4WtsE4WR5GKDiqoq90WxbdPuweT4oYKVdUIfs29QI78dtGqrDaUowhIAUQLIZkoRybR6H442WPRvB6nztMTHJPThKIxUF92tfw8oftSGA7veahEAz2uaUVRNq5KPj+4N/36rjj0lIRI7qmTo1gsAOt+pZ//9xcBCH1t+drjKEfRNmHqKmZDmTpP9jvcs1muo6MjKVYTjiLNUSTr7plWuk8Dp8pz5aR3pv9GsFBkmhAWigzTDNhuDIWDpUJRzVbTgQgtZx8SglULsgolEzVYUk5J73QZhpZWtXJ4NwDDdT2wXTqxrmbstcB2TOiA+bJbk+IN0OFOd90APB6HBs48SrZF2fWSu61DVkexmudDsQD89l9LJyPMYjYLzpCfIYTXfix5fnhzFFM+f//89HYVablBdDBIJ22oe9rTr4/1iz/Xy19+QrpHrmOtqFQo0iqwNEexks9to7M33emcOh9Y9z/p6/jd3cCso+TjXFeyEq2NmUv042kLpIiyiS3qyLj6F7quR0WaC2fmiC1dATz/02QfRNu+dBKhGNJH8oLPa9GQKGbj6MO46hsy95cyvDv9dy6thYyPYkG2AVH87DNy38YdxRoEo9mO266XkteWz9Es11FUqEiBsVF5L5k8W+dnAoaj6Ag97TFa1xz7FuCKb9i3ZzIudBs04VvpfZxpSzj0lGGaAZtQtBWV8M1Wr/9N9fYpFBpqteG3WiRlKaahHBoVRqtmZYd3uUNwbW5j8WB51SQrodwcsVw+OWv/9n8Dpi2Uj20DxigiM+opjiIdRLl64mVFHeP7Pln62o61ycHUYWe4i0NQbGFYlVQ99YXz7n8lGR5JB2O+nGAAWP9gZfmAFYeeUkeRCEXf5972XLYqqNNSQucAeY395iv+9ahm41MO9U/czCSO4o61UhSc+9d62aQ5pTmOnY5cNt9vZFqUQocxUO/IAef+rX69c5KcCDH3ZbywznB4H0n1t7m8vp7N0FN13f34IyiZFNv2fLoDWW7oqdomPca7N8jfc9WupBaOYsi15c1RDC1m4xKKNNw2Po5OR9HRR9HscTpwqn1bNhrpKFbjPs60JSwUGaYZCBWKvtnqlxogFKtRTEPdaJVQVE3TiyNud83lNtY7/LSSHDFaen7qfFLyfyi9t2baQI2KqGrNTKcd44N7gBd/QfatM9l2YMp8GWI6Y7H8jLaBtsLbRzEl9DSteiEgBYhr8BPSYuHZH5d/rIujurUFpezQUyKofZ97aGe2AR8dLJvMPak0V86FEm9T5vrfO2WeHuSv/g/g1guT4u9Vl5fmOLp6OfqO0fYX3K/ZBur7SV/Ks26UFVJd+1IYDs9rpKhQSFP4pV13I/t03qWNckNP07apcsZrIRRDrq2KchTj77aj0/3d2PIyEzmKrvYY5LiZ0RT7t4f/Dqftf62pdlEspm1gocgwzYDtRmJzOXyz1Y3oI1iNYhrjQjEeANM8D1dBG+oMdJP8pHoLxUpyxFRIXEenDIlLFOgwCzmQiYNQR7Faoae+Y7z252T7uWTbgY89A3xoNXDjauCjz9gH2opKqp6qcN6zP6GXmflCFDr4CRGKe18u/1gncmmJu1ZuMZsuIhTV5z71ev86fnc38NVT091FGno7cLp0vZW4z1lypp3EDtjkQ9LfViwA33+f3m7xoBS2D/yVfo8tV9tV9MR3jPZscn9+m1BMTOY4RDQNPc3SR3L872OhaJ4PvuvOzNGmlOso+rYJ1EYohlxb6pg5cxQDHEVXfiKgQ08Bu6NIv9NE6Ck5bs/fk1znr/4h3JFrpKNY7aJYTNvAQpFhmgHbjc3mKPpmq9MGxuWiSr2blQ3VPldaTCOKtFtiOoqAO1SMLqeha2bLjFpTjNr81AAAIABJREFUjoOgUIPQqXNl3k+iAqLhpNIw0tRiNtRRtAhF3/G04TvGVJxXMojM5fV3YOYoRlFYexDaRoA6ADbU4Cck9DTfne62Ae5jTd1vWkwlU44irXpqVMXN5YFj3hy2nh1r093F8ckFAVx/n+wvqMQ9FU2h+BzFkOIvtlBB16SK73qMiu7Pb+tjRz/zFMfxp8VsTnpntj6SgO6laJ4PvusutZhNmULRt01A59JVE99xy3Xp69+VLycCHMWuNKFocxRDQk891W1DHblGCsVqF8Vi2gYWigzTDISGnvpmqxdkyIcIISRvoRKhBMQ38Nh9MHMUAXeIabMIxXIcBEAKH3VzVrmKiUbmhrtA3UFXwRQg3VEsNw/Fd4xpsRJR4W1FuYpm5VQ6eE8r1EF75NlcSYoa/IQ4ijvXSrcrDdexpucqdS3KFoqTS183c6N8uAavaXlgvvNAsfBM/dhX8TTEwbI6ikbVU4XvelTYPr+tj93eAEeR7stYQTq8F/ydXtY3K72P5LijaEwOeb/vlBZK5YaehhzjWhQ28R23nWv1hJQv9NRa9VQ5io78RMBwFG2hp45iNiGRASGOnG2iol5Ueh9n2hYWigzTDFirnlqEoi8fKa0BczmE5C2UK5QUiUIApOqpIiT0lFZNrHfo6dIVwDFvsb+W1jdraKeu1qgGoGmOYnDoaUqOou943n2jfZDiO8aHvZpsv8JBpHLLTJGX+Pwpg2AaHpnmHgB68BPiKALA5seBuUvtr/mO9fg2yYAsS+ipK0cxbZkP2+A1rQWBV4QJ4KIvA6dcqxf5HMUQByvvcxTJteL7jaSYn98beur4LGYF1lweOPZiveyYN6f3kVQTZOb5ECp6bZQrFEO2WYvQ05Djpn4DXBNFqY5iQOhp30z927rX5yim9Hu0EeLI2SYq6kWl93GmbWGhyDDNgNVRtAxeVT7SYWfpZbQHmatCaLmE5C0sXQEc8Qb7674Gw4CRe6WEInEUD2y3h0oOEUHYSKHYkQMuNqpATp2f7iAAOgcG0OIh1VEMFIqJPoqGo+g7no/9h91ZTBvE5bqSlf0qDUtT5eXN0NNE6G2ao0gGcNMPT9+WGvwkQhc9M+e5buDim/Tznmn+Y00nO2il23IdxS6bo1iGULQNXtMcRe9gPpIFb/a9ohdN8eQohjhYvhxF6iiq30iXoKeYnz9NKOZ73aH9tgqsLoFh/XsSekp/w7OIXpNyQ0/Ttql+n2rhKKrjpprep77X5Sim5SjGv6dpjqIQemLD6yhm/H5DHLlGhp6mHfeQ+zjTtrBQZJhmILKEyrhcjlw+6bjRECfbeiohJG+hIweccUNy+aEn+QfPCjpYtoWePvgv9lBJ2tuMhp42oqCPmUe55Jx0BwFIfrdTbULRdBTJ4KHcPooh7o0rJM8cfKvBbdckJELgKnUbqKNIB82Jqq9pjiIZwE2dFzb4odear+jEno1SYKrB/6Gv8h9rl6OYSShmcBR9uZTj+2IZvKYJRXUeqIq2qtDN8mv0e267ONlawecohjhYtsF9PqXwUy4fVsjJ/PxpOYpT57nbfCTCYOPr1iUwbCjnOyomJ4TU963Wn+sC+hekr2v8b8u8DukxVt9xrks+V/edWjiKgDxutkga2/tshOQopglFQP8W79kkf3+KjqqnWR3bEEeOOorVnvQN2fZltyaLT4lc+H2caVtYKDJMM2B1FFMGOioPr2tyMryu2mW1Q/MWzIbfb/2qf/CsSAhFi6P4yjP2vxt3A0TSOaq3owiUlqnfu8X+Pgr9zsaFIg09TXEU04RSoo+icV6F5pjZQhJzeaBvht7GoSfG2ygmt1Nx6GnsKI6NAl97k3aRH7+d7Eugo1gclYOfSbP0sllHlQ5+qNBwhRcq1DnfSVoipFEsJKvCPnqrfpwp9DSlmA0QN26Phcyc44EzP+Bfp23w6mtBQCvafvRp4D0/TrrjB3fLCrGKvpQG90CYa2YTWgkXzzgGY2PAjt+nrxMo/fymoxNFwN7N8nGa+M5XyVEESicPhNDrWvx64KwPpa8LkMLO17sy9e/jYzwrjtTomymf0/6StSIoR9KXo2jcB8fG9HeYFnoK6OM8ekBGArgEv0/8U0IdOfq5qj3pG0I0BuzfRp4XgRMuY5E4wWGhyDDNQGjoqeJA/GPeNyMZ6lftm0to3sJOo3dUaM4XkMy9UkIxS/XWnn45SFY32Wd/kq3BeDUwheK+l+3vo9DcJ1voabntMRJVT43zKjTnyZVPQ/tdqu2MFZLFIyoNPaVhlRsf1i7yfZ/Sy1Md1TzGBVPxYNzQnAzqXv3B0kkMer6e6BnQqXNeuS2jKUJRFQ967id6Ge3jd9BTLZGiQk9Fzi48Ojq0gBzZBwyckr4+1+B1XAwEukaP3w688DP368//NP3vlZNBXckZS4AT36Gf2z6vq48iIM8Zdf30L7Rv1/b5zRyxA9v1dZcmFG35klkcxbTcZNpPtKc/rGhRuWGnJuocUJMHvob31SAoR9KXo2jcB2kOa5qjWCwki6fdehGw40X5ONedFN+u7/iCv0u67VkcOfqeRhS02bG2dLL5wDb7e5kJQw2vdoZhgrEWs0kTirFr1jcrWWXSVu2tEpauAJ79UWlvKCA50DKbDIck9ysSQtHSHsNH73Tgu+/V36Hqw7bht8Cae+UgNK1KaDUox1G0hp6mtccIDD0VQg6YomKpK710hfxOfO0IXPk0ql9Y1+Sk+0IHF5XOPisHJ420gbAQcmBeGLa7O/stAx/6+rJ3AS/90v4d0XNeOVpprqCv9cPWZ92vmShR2T3F7RZ1T5HC4uDeZI+9qfO163fICcAZ75efw3asxluQBF4zvrzX1d/0h93l8sCRbwJW3Safv+ZDMkf1iW/L51ahmFJMZNsa/fiUa4HJs+XE0e5BeW4vv9r++U1HkTqlaSG0iX0pw1FUv3tA6flEj2NPf1guarWEXAeZDAK0eKlFewxFyG9UVkeR/pa6HEU1qfN7Mumx5Un92BT7rt/gpSuA0/+X/TUfJdEgVRL8odh+j/Zvbe2Kp8WC/B1evVLec/vny8mIpSvYKQ2EhSLDNAOh7TEAeRNUoad9M3UCP1D90NOOHHD+F5JCcdIc4I1/kRxoVeQoWqqedvdDukIBeRrRWEBl1qvC96cc9hlCcf8r8jil3YjUIDTXJQU/kKGYjSc/piMPFIulxWyUe3PbRcD6X7v/fv8rMuTTvKnSfpd0UD1WJaFYLACbVvvf5xsI52KhqL4zmvdkFYrkfO3sk9/RE99KFxf5gNBTn4ja+WL66xTlKKYJBZrfSZ2RQ47X59t5fwPsWg/ccoF94JTVNfLlvW59zn8tAIZDOOwXWmnuO3VtZx+lq476UJ85Ksqw0z1k0mJqSkikzd2k12sWR9EMPQ0Rih15YyKpSgIj5xCKtXQU1W/UE98Cfvon8vvoyAMXfhH40Yfj/XLlKMb3QvM+SH9LXY6it5+ncS9yfcdpaQE+0tIG6sG250uXmfe2VkKJf3pcVYRKvSaR2wAOPWWYZsDmBLqE4tAujN+0+mYmHcVa5DWMGgOXWUeVhu6VOIoZhOJBi6PY0SEHRSH4yo6H9K+qFNNRNHM9KKrh/eBD8QIhq42OFT3FbAJDTwEywLNMHOTywEnvKF1O2bHW3mNRDWI7+8igeiwpSMt1G9RN3dUOheITyqqXos3dsYVSqddzXfLcM/PwVMN5es6rY2UeJ4pPRLn+Vp0jtNKvCmcOFYqmo6h44K/T+2iOC8XAAa8vp2x4Z3qPToU5SUKPWafNUXT0UQSSQnHmkenbpSQG6sWko5iWu1qxo5iSm5wQilN1VWAKLW4GVDH0ND7fS0JPa+zEqOtP5UF39gGvuozsVyWOokMo+iZ1zHtaziH+y21LAjReKLocxVYlpL0X44WFIsM0A6F9FIFksZZJs5ID81rkNQwbjc+3r0k+H9lfejMpO/SU5KeFhp/6XNSQ/lWVYruZ2vIUacN7NfAoHtQDdTr4KHEUA9tDAKW5RSb03Jp1jH8gq26q4zmKk40cP6NSYzl4Z/QJXqEcf4/FETkJQ4VsmqPo+x4oIcVsfCLKJsboOULFnKqcSsMUTRJCkbbkIPsxPkFhoI5xMaOjGJJTFjIo6zQqh2ZyFJWLFwvsx8m2XvpV+O9iIkeskAyDrleO4ogpFM0cRctEQe+M5PNKxArFDD2N6uAoUtTnKI4YBbMy5igmHEVH6KlvUsdcp+s7FhUMqxuVo6ium+cs+cT7Xyld1iqEtPdivLBQZJhmIEsxG+qI9M1I3lyqHXoKlArFfVuSg5dd60v/ptzQUyoUsxS0SaMe+RU2oWjLU/TNcK77pX5e4ihSoRjqKLqEIln3BV9I9qF08eg3tLtMQ0+B5Lla7iDSd1On+LahHMXiSOl1ZDtWSpRkqWSYJzmKrlL2PhFlE30+wZxWAEeJiLHRZC/DLNVu1W9RaEhWaK8/36CMDuALQ0mX0JujeDApsGkBmB99OMzRBJLn1epvAo/cop+/9Gv34N3mbiaEbpZiNr4cRUukRa0cRfU7okJx6+Uojm+fXsdUKHocxZLQ04AcRd81Yn6ntu+4o7OyarPCmKioB/S6seVb72thoRjS3ovxwkKRYZqBLDmK1FE0i9nUIvTUbHwOJEO7zPxEoPJiNkC2gjZphPSvqpRQR9Enhp4juaDltscAiKPoGGxQIb/ul8CWp9LXBwC7N+jHplCk6yt3Rj2kx6PC55goR7EwUnou2tqnlOUoWloimCxdASw+270O24Dbd47sSymURN0mdbxER7I3Whq7B/XkQqjgVzllplixrTsNM+za5yiaeYHVCDOjn/knH0tOyt33KbfgTLTHsAnFaoWe9us8bkpfrRxFOhk0ou8vdXMUYzEWjSUnfHx9FM1UjhBH0TepM8lo82ITipV+740IPfVNTA0+XJ/9qAWh7b2YVFgoMkwzYJupdoWe0tC5vpn1Dz0FkkLRzE8E3CLXRi0dxdD+VZWihCI9FjZH0SeGqAhIdRR9QtEIGTOh637+3vR1KaYcqh939bmFYrlug++mPpls3+eYjDuKB0uvo/3bSh3AchxF0wGz0ZEDzrhBP+/ulyXzZyyWz20z+L5zJG0ShrZO2LVBLwtpqQDIgVM5LRByeZm77Ft3GnkzR5EWGHI5irF7UxiuTpiZ7zO7BKetp2Om0FMyQeYrZpPvLs2Pq1noKb3Gh+3Lawn9HDQktyJH0ZGj6HPGzWJGHR2l+1FJIRvA6KNYp9BT33Wz1VLgplUIbe/FpMJCkWGaAdtNwRl6Sh3FmUboaUCV0KzYhOJT35XN0D+/CLj3k6Wvl+so0tlyn0ORxryTs/WvqoTiqM4fm0UKZ9gcRZ8YooORiorZqIqkrtBTcnxCQ4uOu1g/pu0xzPWVW8zGd1M/4W36cUjVU0AO1kv6UR7UFUQV5TiKacVUKLQgyiVfkYVxph0mn5uiAPCfI2mijzqKQ3Fl5N5p6XmNlOVXkxzFjIPeSgdlnWbVU3L+246LECT8d6g6YWYhvxU2wVlLR5GG0aqwU7OgTV+NQ0+B6kQNZN4++Ry0qJozRzHer3KK2Shn/JKvamGf79G/Z7bJipJw1AoFtFlMqR54C26ltP9pdpaukBWPbdRrErkNYKHIMM1ARcVsSE5ELWYhD1qE4vP3yKIYwzvtYmQkpRJkyfpjoSg6kgOqSkJPV3y7tEplraAO7yEn6Md7LULRN5he9i792LxBj5XhKDqL2ZBBbFo1R8WxFwGL36Cfd01KfreJsNgyB0tpM/qdvcBhZ+nn3qqnpJiNLSzUrHxalqOY0sqEQgWKctWUcBsrlF7nvnNk3lL3a7ZCJz39SaE4fZH9b9XAqdw8tLTjFzIoKwk9VcdNuIVPnkwIVCPMLGSSwyY4rTmK5TqKntBToPQ416zqKbnOqNiqd+gpYDiKjuMU1B7DEXoK6Gqrc47T71Xrsol983eoYkexATmKofnLrUhHDjjns8ll0xfVbxK5TWChyDDNQNk5ig0KPfWxaVX4e2klTSp6XaGnR55rX96/UD+uRa6mwmxd8B+kbPu0hXrW35ZL5htML3+3fl5R6KnRONyEOmAnXpm+rqVXyZsqdXhKchRpWFqZN186o7/gDGDqgB5Ad0/N1icuR9pj2Jz5/UaeYqWOYpqDnhCK8TmaKF5iuIq+ELiFZ7hf67Lkr5mVMhecDpxEjnmuMzlwUhMSWcMXbcdvwRnhg7Jcpz6nCkP6/M/3uAuEjFeeHapOmFmIALIJTqujeND+uo2ESE4LPY2dRNNVrlnoKa1sXIWCVVlJOIoBQrWS9hgUde9RrjxgF/tmCHAr5ij6rpuoaG/f1SrsNortLb+mfpPIbQILRYZpBjJVPY0HuaJD3tAaUczGx8tP+N+jBJd6b2EorroY3+RdjuKFXwROuVY/n3GEHIwe8Xq9rFZC0da6gBaC6ZsJTIkLh9hyFNVg+uT36mVq/y+/TQ40aDXNxLbLCT11FbMhA6dlKcIk3wNc/GW532YuaUIokn2rJCzN7F94aOzQFoaNqoeBjmJULHVoAHc7l3JzFNNCT5VQzHXLCAAg6fCZ+6fOkTf9pX19oaGnih4j9HT0ADD7WP1c5PTAaWyssoIlIf0n01DfKXUUbSF/ivFrZbg6YWYhn9kmOCvNUUyEnhoTREoo5nv1eszjXFLMphahp43IUaRCkVwnztYU6jyLdBpGsSBbpCju/bPkfcaG7d5j65tofs+Vfi+NEIq+ialoTKdWtCI7X0w+P1jGeGaCw0KRYZqBcorZ9M6IE+rr3B4j6G88P8a2ktzFUd1PcHQY2OwQm9GYdEUUb/hU6WC0VoUAfBXitj6nH+9eL/M4zUFJLg8MnKqfn/Pp5P67GrlnaY/hDT0lg9iuyaVOkHJFx4oYLxhChWKnUcymGu0xbNDQwkTorS9HkXw/Zj4ikAw9HSvqdZdd9TQl1FrNaPcPaGesK6V4CSA/39EX2tdnE4Pjr1lEZE+//D7UcRnZnxwsFYb1uUavm3qJAcr4uX9Af6dpx0S9VhiW189ZH9GvdU3O5mgC/ve4BCctxGPLUXQ1Z1eEhJ7SvMQeo0WGGXparWPX4RKK9cpRpKGvGYrZAPK6VvcZ+pv9yjP6PuOqCm2LZrE6ip3pz7PSiNBTNTGl8uNFh7xujjhHv8dW1btV2GEIxXImvic4LBQZphmwCUWnoxiHwyh3ohlDT7tS8kAAfyn7r58HPHKz/fXRYbu7VmtnFfBXiHv6+8A2UiVu8CH7oCRR6t0YgFBXhUJFX2h7DF/V045OOVAwnaCjz4//flQ3bk84ipOSYq0aVU9tUCGQxVGlgzpbji3NK83i/CS2YeTU2RgrAns2ycc0ZDEt9NS2X5RUoejIURRCi9OR/aWDJeUYJM6xBghF6hCOhwOnHJNOcn4AwObH9GsX/VN2R5N+5rM+rB/3zUoXnHQfrTmKWYrZOEJPqTisW45io6ueOhxF1/YT94Bi+S1TbI6ir5cnUOUcxToVswHkb7n6fT30VfK6Ofy1+vX9LdxLcee65HN2FDPDQpFh6oWZ23bzudptsoaeOtwg5Yb0zZT/J26ONah6Ws4Pq6tghsInuOiAz+TZuxxCkTqKNRKKvgpxIxb3CigdlFC3OG8M6qirQkl8Zl8fQU97DF9O3uQ5+rHKtUz0uzRDT2lFxGoKRTIQSxSz8H1+n6NIchSzVKekUEfRJRT3bdHHYNoCvZwKA1toLODOUbblISpsQlENetXfHdxXek2rySd6vjTEUaShpyocOCWnTL2mxNmm1fq1+cuzb59+Zlo86Zg3pwtOmvemnNDEeeWZgKCf0ekoOoRirqu0qm3VQk9dk0GNLmYT6CiW2zKlbEexBaueAnJsoibPVM9Veg9oaUdxbfI5O4qZYaHIMPXAltu24bfEbbKIQpujMHJACwiVl9KMoadTPJU0szRXN3nmLnthFyqYy73Jpol5oLIKcXRQkuooukJPMzhqasDhDD2N1+3K/6IN2pVQpMLVLGZTrJGjSL8bKlR9AzI6qLMNDOjAp1xHMdFH0ZGjmChkQ4UiEXuuiqkuoXjPx905Vq4cRbrNkX1uRzFRMKhKBVGy0Enyc0cD8kbHHcU4v/mp78nnHXngxf/J/jtAz2l6XHznBT1P1fmUxVHs6CAimWw3irSoTwhFEoba2VsqpqtWzMZR9bSak0Fp5Bzb9+YoQt4Ly22ZEuootkOOIhBPPseTzEogTpqtX9/XokIxithRrAIsFBmmHvhCYGjT+vFBvkUo0ipsfSr0tNbFbHYnt2fSkZOhTwOn6WWuQa6iEsG1d0tA6GkZzqpXzBf8FeLS2PqcHrgmHEVX6KnZHiPDIH7cURy1fxejHrcmIRTjQUIi9NTIUSxUoT2GDTo4ozd4bzEfGnpqcRQToadlOor5AEdx9wb92BV6SgUwxRV6uuUpd45VqlAkoafmd2ITio2oCqi+lwIJPU2rUqmOwVhBfidqAmSsANz9wfQ8NBsuoeidmOnQ59yo4SiKXJjTZIskGNmnf9NdjmJnX+lvSE1CTxtd9ZT2UQx0FMttmWLr4Wt1FE2h2IJVT4Fkhe7Jh8hrZsPDetmv/8lfAKgZ2bfF0pfUEfXDOGGhyDD1wBcCs4uUcFaDJZsbRAe4ttDTav+Q03zAhWfYS99/aivwp+uA6+8nbQlSqkAClQmuSbPtQrGjQsEcks+ydAVw1PnZ1w3InpNq4JpwFF2hp2mOYmCOIuAolORxa6yhp2bVU1o6nxzvajbjToSeBjTcHv87GnpqmUE+UIUcxZDQ010OoVhJ6KnClmPlylEEDKFoRAlYcxQb4SjaitmkHBOfsE/LQ7NBz2l6voUIr/F8WsNRDJ18UAVt6PlAozmoi9hjOIqmmK6Wo5gIPaXtKerlKGYMPTXTD8ptmWINPQ1wFKvaHqOOomwfyUHsmyUnTP/7C3rZnk3+AkDNiFnIBuDQ0zJgocgw9cAXAqMGmiKX7AFHKRaAJ+7Qz5+8szTHotzQU1fIJXUwe6f5S9+bgyUXaSW5556U/rdLzvGHnoZ8D+Znvufj6e9ftVJ+1tP/yL9uF2rgmiZOxicKRowCOGWEngL2mWkl7LKEniZyFM3QU+oo1qCYDSBz6xRZqp5aQ0+r4Sha8tIU6tx68J/1ssFVuhLj+t/q5b/4gn223ncNAaXXf66z1CUeF4qT9b4O7Uq+x+ooNrCYTTSmz6m0HMWQnniuPDQbCUeRhjoGCEUaBgvoyaDQyQc1eUCds0QPRVfoaV/peVuL0NNGOIr5Ch1FX99aV8sUa+hpPdpjNKDqKQDsfVk/3r6mvAJAzUaxAKz+ZulyW3EzJpUG3AkYZgLSP1+GMrrI98iBRUde35Do4NtW5nvXS3KW77mf6GXlOGm2dauwy8Vn62W2WdaSz9ENHIR/kKtKcv/8b4FfflEumzoAvP4TwKuuAL57rftmteg1wIYH9fNyqp7aPrMPlc8S0iMyjVUrgXlL9XOXowjIQWcudomoaPSGXpIB3tgoAGMgOd7M3DHQnkQdxXi22ax66qqIWNViNo6iNN7P73AURYc8N/ZslK1Llr8bmHkE2V4WR5FWPSWf33VuPfBZYNMjAATw7I/0cnUd3/9p4I2f1ZMvrqrHFFuOVfeUpHBVg95ukhdJB4ZAE+UoWqolV+IoAu48NBvOHMVyHMXh5HIf6rO7HEUlFIsFYJCEBe5cJwfEuW59ztS86mkDHMWgHEVjslDdZ/7vUbKAVUcemH+KdBJPeqf7cwQXs6myoygaJBRp6On636S/d9VK+RvVzKTd34d3A4VRIN+A37cWhYUiw9SDZVdL4eWis0fOdHU4HMW0sEgqFMsJV0lb99qf68dpjb4VKk8nxA3J5YFFZ2mheOYN+gZ02a1y5nLVSjnQy3XqxrlR0d5T0Ff1tFiQn3X1SmDr8zIUNAsqdJBWViyH3YPAHNLwvMRRNNouqHDCYoY8QOoEpBVKcg3Ce6fLbYwViKNIBrCdplCsQ44idTS9oac0R5EIRXpeDD4k/y080749H7ZKl0D69fTsj93rG9oJ3H0j8ML98vx39VGl2HKsuqcky9mboadAsicloCMHGp6jaJm4CMlRTMOVh2bDmaMYMIEw3tpD5SiqayxQtKnjQ7dL3fCefvsAeGSfnGig10TVqp7SYjINbo9RTtVTQN5n1HU/d6mMhPER3B6jXXIUye/F/h3u9wHZJl4aha/f8arbgNOur9/+tDgcesow9SAtBAbQg3EqFKkw8OU4KspxFEPXbTZ5tjHeIN2To6ig4YTU8TD7+r3uT/RrxVF7vl5arqZZqCarSATkMVy1Enj2J/73ptE/YOy/I/QUSA4aqxV6GkWk6qljEN7RoV3FcUdxn97fXN6do1jV0FNHURpvMR9PMRsKnUHP1EfRkaMYej25UOFdIY6iLcfKzFM0Q09tNEvoqe18DKl6moYrD81GIkcxQzEbui/qWsjsKJL8TFWAynQU0wbAY5Zw/EppuKNIhWoZOYpAXDk2/g3oCZjsBICuKaW51kGOYhWrntaigrkL6ijSNj42sky8NArfb7AtJJVxwkKRYepBRw5467/53zc2pmcph3fp/LlQFytEKJq5eaHrDhKKgTmKCjqITxvImg6Zt+qp8T34Zhh9HPNmYM19Umj6BvC+HMvlVxs5iimhp1SA2PIyXeRShGJo8RZV0MYsZqOcD9egpqrFbByOou/z5z05iiHb8+E6TpW0flGsWul3FF05Vokee916P81eexSVs9gsxWwoIX0UXaTlodmoSuipEooZchSLhWTLlq+9Mc4RJxNaPf3hkxA1Dz1tRNXTjI6i+k0qDOvfQFuxJ+t6Okrvd9ZiNsY1UrGjaHFE64GaDOzsA5Zfk/7eLBMvjcL3G7xnU332o03g0FOGqRc0HMxFcUTfHId3p4er2vDdXMrJzVOEzMY5ET9bAAAgAElEQVRmdRTp4D/tJk5vyMURe+hpYpBgtIQo1+XpnQGc+1fye737xpT3TQdmHS1vomk5lmrg+vw9ZP/JQLJYALat0c+/cx1wxvulm6kcA9Hhn9FPCz1NFG9JGWirgjYHtst1jAvFWNDbBmsiBwiRvm9ZSOQaZgg9zeIoUsoOPSXfqS8fOYTdg8kJiVd/SP4W7B6UM/ppOVaJ6phksJsqFJWjSH4/GlLMpgqOYq4bmLfMn4dmoyqhpxkdRfWbTCfsNj4i/80+Wi/r6Q+fhKiWyKe/u7XKQ07dftYcRYvQohNFoUIRkL/pVKjXI0ex0e0xJs8Blr0LeOG+9PtXs+P7DZ7kaPXFWGGhyDD1ImQWKyqW1+BeFenwOYqVOGtZQk99pf0VdBCfKhTJDXnMFXpKBIoZtlOuyzP3RBkCe/O56e+bdXQy90XlWN73aZ3/9eZ/AE6+Rg5cqas3HmocDxhpzukrT0sXc829+m9C3IJEMRvTUSQDvrTQvfEWGZGsEjqa4igqqh2SVg1HMZNQzBJ66nAUffnIIfQPJM+RY94MnPuXYX9LryOaaxUUekonYJol9DTlHLW9dsLbgbf9a3nbp+d0IvQ0QACofSkejMO7Ax3FtN/krc/pxz394ZMQtah62pAcRbL9kYxVT9W9MHGPCbiHKcyCNlZH0Ti2FVc9bXCO4uRDdQGgJ74lK4Ef3CsF+MVfzj7x0ih8v8GLX1e/fWkDOPSUYepFiFDsyCcTy0M49iJ9g/HlNWR11mYu0Y9rkaOYaLmQMpDNVRh66mu8TKFVMNUA2ic0zQR/lWN5wtv1siVv0DdZKvpU/0dfH0d1/oQIRXozHx1Khhr/5xX6tbRBOG2Rsf8V4ijGOZQuR7GaJPooZhCKdABntq5I3V4GRzHXqc85KhTT8pGPeYv852P51UknOLQgCpAUiglHsUVzFNMmM2zHa8qh5W8/cd3Q0NOACQS6n4XhcEcxS454aP/ZqoWeuvKQmzT01JanfrBcR9EUijZHsTP9eVYaIRRH9gMjsZhWk4Pq/jX/ZL1fZiusZsZXE2LesvrtSxvAQpFh6sWeAFere0q4GwcAS68CLr9ND9B9oadZnLUzbwAOJzNvIVVPx2fVR2S+pY/EbG8WoWjro5hS9TRkgJXrAi75KrDkTXqZGkD7hKYrwT/Rj5BMAKhjTAWNb8CowoNCBiPUCbj3z3QRnz2DwObH9WuukLpiQbZtUHz3fTqEq66OomP/slQ99THnuPL+TgjtKpqFPi67FTj1uvFFe/sW4qapH8Fr1l6Dy3f8ER561WcR9c6wr1eFd9HQ05DQR4VTKKaEno4ekI5RU+Yopggt2/snVyIUXaGnHuFVLAB7Nuvnt1yoH/vOqdDf5O6p6QPgPhJOV4uqp4lzvE5DR3re08mYEKEYVSgUgxzFWlY9rVOOIr0v0fsVkCysZ6ZzNDPqN1iNCXLdwKyj9Ot00pHxwkKRYepFiKPYMy2sibRi+wvyR1HdIH0/5lmctUdvA575Adm3DI4iEFa1keaddQWGnhZHtNCi+XppVU99M4yAzEdcdlVysKyKfPiEpivBfzLpR0h719lK5/sGjOpvQgYjdID34i/c79vxQukyFQL75J162bbndFhiZ4qjWMvQU4rXUXQMlC/6cnIA+OZ/AI4nrm8WRxEglSoN1zKXBw45YfzpR3ddhi++cioG94zi4fV7cMXDR+KGud9G8aKbkvtz8VfkxE9HLlnMJlTAFgvyN0GxabV0k8eKdqFIxerQzsbnKGYOPbV8LxU5iq7Q0xThpa6XwYf0sk2ryDo9oi3oN1kAT35HPrzsVjmhteAM2Xt2wRnyuXJ/gNqEnjbEUawg9HTcUSSTkaFVT4EwR9FcVnHoKb2H1clRDBGKiOpbXKcaCKEnC5acA7z5S/q1LOkIDAtFhqkb1FE8+xP68eRD9WxwRx6YsTh8nSrkUd0gfaGnoaFLgJx1O7BdP7c1wzZJhPwFCMURR3sME1fVUzqASws97cgBl96SXM/0w4E/+BhZb7y/1NE9uEdub+kKd8hgWoJ/wlEkJchtjqJvwKgGIUGhp4EDlpefKl3my2M9sM29jVqGnlJ8n8/2dyInc0SpqJ+/LHm8sziKgBY2tlBrUghjZ1R6bv/kma34bnQ2cMTr9cITLtXXctGSx5rGeKGqu/SyA9ulm3znNXbBNf2w5P42vI+i5TcmVShahGW1hGJo6KnvetnrmQAK+k2OgLs/KI8jkGwddN298jkNfa2WUKR5qvT3vBGhp/TeFlLMJrIIxUocRZurXxJ62oI5ivS+RCc2gdIicq2EWd2bThJkqYTN1FYoCiGmCSG+I4R4VgjxOyHEmUKIGUKI+4UQa+L/p5P3f0II8YIQ4jkhxHlk+clCiCfj174sRDXL6jFMnVBCsXe6FB6KY9+iB5wduWSIhA8V8qhEkm/WL8RZc/HkHf735DMKRXUTz3WlD8YSxVn+P3tvHm9XVZ+NP/vsc+6Y3CEDmRPmKRAygQhCo1XEOpZAsFSrfbV8fghqayut7fu21tYOVPtWBe3P4lxRAklb0RKgIIqIGBIgEBJIgAz3Zs7NvTd3OsM++/1j77X3d629pn2GO8B5Pp98cs8+8z5777We9Tzf50usp3QiIQsyoCiN8GEdl/wBcOoV8W32mmJK6NhA8Np0RTLbGq/kMwVIBjrw0pVbmaJomjCySY6V9dRywiIbME0W2BP71O8xHopiJmdOVpURK/Zas8+Ntx3ZKaTAplQUo6TLkeR9I3Hj6n7IF0HWb94vhOKQzyILPNLBVOO657Hk9i6RKNahF18ayPa/rkZRdl/NiCJN2dTsf9P5cvxV/f1prsmsx6YI0fr68OdiJbkaTHh7DMUxqLrOcIpiOAZUnHo6Sa2nYnurb1xV+W/tlfhrxhO3868lOnmmEsTrOi2dyTeIYhrUW1H8EoBNvu+fC+AiADsA/BmAh33fPwvAw+FtOI5zPoD3A1gK4GoAX3WcaHnoawBuBHBW+O/qOn/uBhqoPZj1tGNBUnljq4cZN52qwdSRyHpqqAtk3v2ZZ8XbmqcDl33c/F5bLUIXskKggwlMUdQFbQAS6ykjimRgltWnUAwd5W8XhvkJfkmiKAKxMnSSTMQuvSleydeRI6WiyMJsyG+tmzCe9+54Umwzgbed5LdJ6uRMFli2z2Tfu9YTSNkE3SqBUmYTC19r9nnxtqM77ftKysB+k6JeUez35RPUA/2jQj9GcjxySqcFUTQRlp3/ndymVRQnwnpaA0WxqhpFcg2xJYrG82VYfz+7Jr/3DruFCvE6LLO+HnspVpK9KpQpVerpRLTHoFARMqOimMZ62s3fngztMdhvTWvO9z9Z2W8dlRiQBWDxuBGzAaYSOKLYzJfONBTFVKgbUXQcpwPAlQC+AQC+7xd83+8H8F4A3wkf9h0A7wv/fi+AH/q+n/d9/1UAuwFc4jjOPAAdvu8/4fu+D+C75DkNNDA1UCrEilLH/KTyFhHFbNLiQmqdOFDLo631FAjsMXQS2rkIWLDa/LyjL5pXLStVFHW2U0Cdeqq0nkpqNcU+loVhvg6pXAyepyKKA/vjbarwGhHtKkUxfI+soIhe+23g0o/F27pOjVVLGTlWwXZl+9Q3JbeZLLBswB0X66lFk2sZZDaxSFEkvemOVqkoMmIjS1blFEV5kMz8rlZ1P8ZSyjAbE2GhNbIMoqJIJ5pTIcxGnLy3dOkVSBM4RZEQPB1RNJ0vNuSEpUye8RbzY8WEZZOSLFMgbaEMs5looqiqUTSlnqYgihWF2VRrPZXUWFLU8re2ea3XjKLYyqvJDUUxFeqpKJ4O4CiAbzmO87TjOHc6jtMOYI7v+wcBIPyfzaQWACAzMfSE2xaEf4vbE3Ac50bHcZ5yHOepo0ePyh7SQAMTg6FDAELyIhJFLx8PCplsUj14x23y8AJqebRVFIHgvWiwzshxYKzf/LyxE+ZVSzqYRjV/GqsMC7PRBdkA6tRTpaIo2Q9i25HiSHK1nwblMEREkVyGuhbrPy9DtikODDEpikAwYTyLpK5eelOsWsrIsQp0wrHwEvXjTl+T3GaywC68OHwPmfW0xkOKbBXfhsDIFDi235qnAZ3h73dkR3WKothknSLsnznot6KkaFm87uJFloqixecyEZauRZJtk61GscrU0+nzqnt/ekzTa4hO0TWdLwtSRPHPOsv8GHGRyqQk2zhBVODaY0xEjaLiXE9Vo1gD66njyusPx1tRrOVvbfNaU5koFgVF0c3FC3sNRTEV6nm2ZwGsBPBx3/efdBznSwhtpgrIik58zfbkRt//OoCvA8Dq1aunUJZvA695UGKWsJ4WYqLouMDRl/jnHtkBrP79gDCoELXHsCCKQ0f4WqSRvjjd0wS20qj6LKKiGAVskJVLZpfZ9UDcv8k0gHNhNgWzoihbjRWJYmEkmVYpJYrhvumvQFEEAvvpaJ9CUZQRIcVkQVaXqQKdsFz2ceC/bo4nTO2zgeFwIU2WhLn8huC3Ua02L7k8+TkZpoKi6JXi461/b1CDqns/HRhRYYs9dGIdLjDkc12ARFy/eulcrF25EHicEkVyPLKJueNakTZv+Qfg7n8Svs+XcLLb3orfg3vg6fjcd5v5er7RPn5yPBE1itX2UZw+R/44W6gIkNuMklfGxq29uPup/TjYP4p5Xa24fvUirF3xO3B158uCi+3ff6YFURQTltP2eE0DLvXUoj1FraFUFG1qFGsYZmObvlzTGkUJUazlb23zWovIsTulrafh79fcESzG5Qfkz2lAinoqij0AenzffzK8fS8C4ng4tJMi/P8IeTxd8lwI4EC4faFkewMNTB3QxNOOBYHywgYFj1hPj+8KJukU//3HZiUvjfVUHEzKxXQDjG7VkiOKY2Z7C1MUjdZTMmFQhdmYFEXReloc5iPXgYDAlWpoPQXiQJuhw7ElNlIUJRMhE1G0maTRCYtf5pnDvOXx31KiSmqmuiUJvOy3Go8aRZsm19LnycJsmuKFiyPb4+1UTU9LdHU1uaH1dNbsuehuiz/zhfM7cNu1y3DH766EmyG9GAGeKHqaxQQJNnpX4n7v4kTOj+MA93sXY2P5Cn5hoKWDnzg/+XXgsS/GtyeiRlFWcziuiqL89y85Wdxy19O4dcM2bNl7AgcGxrBl7wncumEbbv7Bsyhd803gTZ+Kn0AtjmnaHZmCzGQJy5X2eLUBZz2liuI4qc1p+6hqaxQdcy08g1cCXv4puZ2XB8aIn6/axRX6+WVEsZa/tc1rTWVFUeYUYcmnDUUxFepGFH3fPwRgv+M4rCDkNwG8AOBHAMKMZ3wIwH+Ff/8IwPsdx2l2HOc0BKE1vw7tqScdx7k0TDv9PfKcBhqY3GC2y4c+G2878kIw4LAJEK1RpO0oKEz1B2x2aGM9pYSHoe/l+O9mQ79EHakUJ84mewszBxjDbFQ1imS7KfU0oSgOJ9MqtdbTcL+1dKVbmWaBNuViaO8rx793KkUxhfWUS4kt8YMmtcDKJuZAXDO19s7kfYxsjEvqaYXWU5WiaGplYJPsS5FTJJb6fnTcOG3dASEM8cXrl2Pd6kXxtpxBUbRsnv7DLQdwS/ET+HTxRmwun41efyY2l8/Gp4s34ubiJ3H3Uwf486y5A9j0p+S9h4ETe8grTkD3rGprFMU+cGmhIMf3PX8cm7ZLajwBbNp+CBufPQwsWxdvpEqtTRARg8x66jbrE5Yr7fFqA2Xq6TgRRWUtok0fRSH1tHm6nTWeLSb9z1+S1yrJA2NqXqNoSD01/dbDR+wTUG2OmylNFMnxyq4rbNxu1CimQr1Hgo8D+L7jONsALAfwdwD+AcDbHMfZBeBt4W34vr8dwHoEZHITgJt9P5JHbgJwJ4KAm5cB3F/nz91AA9WDJpQN7Iu3P3F7MOCwi3Apb6cE6pS8yHpagaIIAH2vxH+b6mR0q5ZZwVJrsrcwpAqzsbCeSlNPJdbTgkgU8+YaRVmtlw5iiwxTfzyZfapcjr9T2hpFr6gmiqbgDxr8wqAjirW2ntr0LpNBmnraYl64+PkX08XMc0E0hOSNDcS/V+sMjBXjhYvRgvD6qtdIqSge7B+FBxf3eGtwXeGzuDz/FVxX+Czu8dagjEyQsEoVxXIReFEzlMraadQbMlKYJvW0ljWKBOufOSzdHt2/eT9vVeQWY1LYmdtmAG0z+W2Xf0KfsGxKS1b1eLUB3R/0ujhearPjpEs+lo0BecvyBoY0gTGJPorVWk8liiiFqZVK3yv2Cag2vYFfS6mnQKz0jw3KA+8akKKuRNH3/Wd831/t+/4y3/ff5/v+Cd/3j/u+/5u+758V/t9HHv953/fP8H3/HN/37yfbn/J9/4LwvlvC9NMGGpjcMA04bCCg1lMddEpeNdZTIK6/c1xg5YeS91PoVqhdwXpqsrcwmNLouJXNGlpPE2E2xeSAOHoiUHpYXV9nWqIotMgwhafIFEWuv516klbyyli/eT9ueygm/lt294Ir6x4mQV+mSWzzND7wBIhVqfEIs3GzyfexSn11k6TVbTIvXJx4JV3MvMo2OhonnqJtBsaK8XlJ/wagVhSjxRA7ojivS29xnN/VyhNFU13y7v+xet+aIpNJHpM6opzJ8NeAOtUo7uvXHw8H+keF+H1a95oiIMkrJdM280P6xQtqFdcFnlUCm9CYesO0mKbaLtYo2iaepgmMqWsfRckxx37r09foX8cmAVXsDZxrSx43rxVFkV1TmPXU9+S9bxuQYgK8JQ008DqBacBhVjUxUEUFnZKXJvVURhQj9aMLWPG7la9Qi2E2JnsLg8l6yq1sK6ynXH2KZC0p0UdRoiiW8rziBwREkRKMqojiEX7AlZEA2WSBe45cUSx55aiO6sWj8TG16emX+QdyaY4Wascp5/G3WXKcjLDWQ2kQ95HtZEycoGdb7BYu0sTMU0WWnscjcQ9Fr6UbpXJ8PI4miCLpHSi1ntp93+tX64/LdRcv4pV7k0IwfMzqfWsO0X6qq/HzSvx5/7Pbqms0L138yGJut7y9CUPU5kR2bNoqisyB0iecr09+zbx4waziH3kA+NR2ux6vNkhr/awHZMe/VY1ieJ1jwSW2imKawJjEtamG+1u1eOxmgZz+eARgl4BKLZirPpw8bqY0UZQsyNLFgkadojUaRLGBBuoF04DDyJlIVlTQKXlpUk9lNYoMLV3VrVCLNYrLb+DDUygWvzH+22Q9dZx4cqC0npIUD3Gi6PsSRVGVeipRFKl1OG04BGc9rVBRpJ9JQRQ3bu2N6qg8xL/RNEfSuoHBJmhDJIrjaT0FKg+MEPdTtsl+4cI2Zp4qinQFezQmisUmXiGiNtTgNRRkM6X1dO2qhbh6qbzZfJSwytUoGs45etyOJyhxBtSKKiNW1K575IXqGs3Lrm1ukx0Jdxw+NZbBVlGsZz/ESqEiZONKFA2BX9x2wbrp++mtp2kCY2puPbUgigDQv099H4NNQB3NRGibkbx/KltP6bWUXaep6k/TcBvQokEUG2igXjANOGwApPbH6fPljzUpeakUxf38+1OwiU6lK9SiophxgdPfzD9m/sqAdL7xlnibTRpdVNM5hshKaWs9zQ8mUykLQ/Z9FLfdE9/e+p10qoVoPTX1x5MFGnCKonwycvdT8QJAkRDFNmiIos0kdqaQxLjhIyGRknQuqkfIhajIWIa7SBVFU40Pg20KsMo2Sqyn+Sbe8pa0npoURbvv62Yc3H7DCrQ3x79Ba87lE1ap9XTOBfoXPE9Rv1Rv0N/bbVbbmetBrGQExG2yI+EAPxFlsFUU69kPsVKobO7j2WMzDVHkUkNDeyEbC1osradpwoFqbj2VWGdlGLAgijYLmiPEIt8qI4qvIUXRKwF9r8bb7vlwde6D1xEaRLGBBuoF04DDVuyporjkssqUPDaZMtUo5oditUNUigD5RCcN6OScWTj7dvOPWXtnQDppjYBN/QibtBSGyDbL1FPRdgoowmwK/AADBMT62bvi28d3p1MtaDjF5juB9aQG1BhmI1EUFZORg/0xySj58USqXUsUDYqiVwK2/YDfduDp4Pv/5FPJx9dDaRAJn+17iEpUtjlWy2dI2n5Q2KrGSutpPAErZEVFUSSKVIWnimJ4HKaocRsrlTGcj1+/ozUbJ6x6Jb6f69EXgXkXqV9MFXRRb1DirCNZ9SBWCqLISPhlZ8Tn8sz2Jp6EA8n6QsCeKNazH2KlsFHu6g1xYczJqBcPxDGA2gttFcU04UC1bo8hu/aLGBvka2BVsEm7FWqpE5jSRJGm9OYC98GLP4m3HdlenfvgdYQJaJTUQAOvE+gal5/3bmD4eBBHzwWV5AISpWpor4JN6qlXAn71tfg2nTQyyCY6acBZT8OJ7jGBKDLLB9cIOYWiSMmdbeqpaDsFgom4aD8pSaynqgGSqRa638or8YSqOAIcfi6+LSN9FdYozutqxYGBYHDkFEWd9dREQp79AfDqz+X37Xowuc2pw9pjYjJmqygKj2PE0c0GPe9+dEvyOQy2LQUo0d71APCLfw4n/KQmMdcJIJ6wJmsUVe0xUrRDCbG/j1/46BsuwPd9OGUvmCjtfTy+8+SB4F9zR1yrNG0uMBS2gUgTwlJLUOKsS+WtB7GSEaNwP2TdDOZ1xr/VyiXdWCdaUqWKouV+7FwADGo+czX9ECvFpLCeplgootefsieMMZaKIltM2vbDYLFhoCfY9ys/GJBESuZEYljT9hgK8kJLR2aczieWM1BC65WC6/jT3wvOmc4FwSL28hsE6+nM5OuYrKe61x7PxQQZ6ILvqz8zuw/SzrleR2gQxQYaqBfYgPPwXwO//HKwrXMxsOZPg4v4v69NPqfSGi+T9ZTV89CL5bBEZZPV2KQBZz0dCwZrMZyBNbmng3ga62mB2EW5MBuN9VRsjcEwIgR2yKynOmz9nn6AefYHwM4fq+8/tjO5zVijKJ+8Xb96EbbsDdRij5hFlIpitgWJ7uwijH0wBdTFelppjaJEUWQwLeLYthSgROapb0ofMuK0gxLFZI2iKvU0vaK4TyCKRc/HyXwJHS/8UD1RooEWZ70t/s3HkwxQUOKs++71IFaKGkWG0WI8eR8YlUycZUTRMrUWKz4I7H9SfX81/RArxWQMs9HZO8XFwkqIIhCXXpjIg7iIU9MaRcWibz8hihf/QeDwYP1fZ50TtFNhhFY27g/2BMfZrgeA7tPi7Wmtp6bXvvbb2oTumkBHVKk746UH9K9jGsdf52hYTxtooJ5ws8DcZfHtt/xFXOsnsyRVOtE2tccwNRpnqLWi2L83OcAwokctpDaKIhtEOaJIFUWd9ZQQxWmk1oiuqAIhuU1RtG9SLUxE68CzyW2yyYKoOktA66hKZA1wmo4ommDbB5OhLmE2wue0naSKiiIlHbVqKSAGr0jQvI9XZK0URa8UH8O2RANJRREA+oYK9oSfnpMTpQhw1lONNboejeYV1lMGausdGJFcJ6oJs6lnP8RKkcnIXQIT2R5Dd/6LNX55YtG0tZ6mQV3bY6iIIqlP7D4VOPvt8e0rPsVnCZjqeHufim+ntZ5OdPgS7VO9/8mYpDI7KV10Uy0UM0yErXsKoaEoNtBAvUGbL9MkQXEiC1S+UmtKPbWdKFarKNKBpZRP2k4BoMCsp5Qo2tQoSsJ/VNZTOsh6JeDlh8n7k+eLhLIghNuYYFItTEQrL4noltYomq2nrI7qhjufRP+eeNX5nBkOICtpsUk8Nak2IsajRtHWiqlTFAF71UD72cxke8Yr/wXgT6LbeW2YTUj0aIsW2XVCAVFRBIDjwwWcakv46TlZ7aS3UtB9qiNZtVKFKaTWU6IoFghRtFUUbWsU01gexxOZXLJl0ESG2ehUKm6xUFQUx4EoVqugcWOYynpKiGLXIp4E0XAawDzuH9kR/y1VFDXWU9Nr//yLwXHr+/Wxp5qI6qlvim93zAOGD8sfC0yMrXsKoUEUG2ig3qD1cTQB09RDLw1M1lPbiWJNFcUx4Piu5GMiRbFC6yn9jkrraVgjxlYdX9pE3l8Ti00VFRuYVAsT0WrtTm6r0HoKBHVUs6c34xipUZyRUyikNkqHyQ4noh4TSPE8sbaeitH1dai5syDbTcMHudsJRdHNBceuX44DGGh9TYrPLSWKQ3l7wk8n1hNmPSXEWbd/60GspIpivP+HCxVYT9PUetZi8aLWcGVEcSKtp2kURXI826aepkGtFUXHCb5fuaQmitR62rmIHxtIWx4A5nGfjcW5dnk9sE5RNL32iVeA9R8E4PDlF7WypxpJ8Ivx38veDxx8Rv3YSm3dk7lGs4ZoEMUGGqg3hhREUaoo1sl6ajtRrGWN4pGd8toAplrkU1pPZQMKHcgykhpFW8stAyWKjqtPkbVRLUxE69QrktsqDLNhGBoroUSIolIlNSWeAnrV5tx3JesvxyPMxtp6KmmPUWtYvOZI6zyAzN8SqaeOE5CjwhCxnpLJX5WKYt9wwZ7w04l1tfVWlcK2RhGoPbGSWSpduaI4WvRQKJXRlCXHfDWpp5MVsjFpIvsoamsUa5B6mga17qMImIkiC7Np7gzGa2oZHRUURdO4z/aXzHYKCERRWBixmVPs/In6vmpDZIwkmIzlK24A9j2udh9ceF2w2JSG8E2GGs1xQqNGsYEG6g1mPXVc3t4hVRQrDbMJQ0kYQfJKwYXvG1cB/7zU7NFnqLo9BpkU9W6Wp40yuyWblDoZq1oveSsJlaIYTujShrFQ8kp7zgHBRKNjQbpaNlPfvtOvTG4zKYqGVeuTY0WUfPK58gqVVJcoGb2XppZv3XeTE8bJ1EfRZD2tBSyO25cXvo+7PSqG2QDxd4yIIlUU7b5vueyjpy94PiUvx4cL+uNw9rnx35yiOFE1ipQojjPJktXkEaJOFUVAoipWqyhORkiTmSeyRtGiTRRQeeppGjiOsFhZA6KoSjBnY/rBbd0L45MAACAASURBVOH9xeB2MznmREXRVMfLiIySKFLrqaAoml7bBtX0BjX1qebSk9vjcYwu5rzr/wJrvxH0BlbVOqpaZ9jWaIpzsW9cNeX6N7426G4DDUxmMJLWPpsfyKRhNtXWKHrylS5bVGs9tZnU9oQF9GzFr2m6OX0TkA/CptTTtGEsdBVSrB/MnwROX5NupZARrR9cD+z+n2Ab14JAouqJTaMBa+spAJwcK3HtMZR2WttJuE61Yavf9HatkSCKlpMxXZhNrWAi25kcSqUSMiijHK7LJhRFICacjCiWqIJs/twlr4xv/uJVFLzguM9lHLBX6Bsu6G2ao/3Ag38RPJjasieqRnEiiSIQHMMKBX+kwP92A6NFzJ5Ofh/RkeE2213bJjNk59tEWk9153+iRrHOiiIQHB/seKkFgWb7ll5XZWN6cSQgMy9tit0vIlFcfkNwvyx5+9x3xSUZsvpEQG89XX4D8MQdwNEdqBjVhMiYXBLT5wb7w3Hj8XrFB4DD24FffTW4vfiNwLb16VpnMLvppj/Tf76t3wssr68B1bGhKDbQQL3BFEUaZAPUNsyGWk9t7JZuU6AMXSC06KjWeprJADBMjA6FK6JM6bKxnQLyCYIp9dS06ihCpb4xVJLm5maBRW+Ib5++Jv5bRl6oqlGB9fSkaD1V2WdrMQkXyURdUk9TpB5S1FlRLHll/NfzffoHlYu47IXP4Y7cl+Ai+B3kRDH8LUoSRdFgPS15Zdxy19P4u/vjVivDhNAcGwpfixH+jzwAfGp78P+KD/DK+aSoUZwERJEiPN9835cSRQ6iojjVbaeA/DgYT0UxjfWcfK5n9x7HQ8/EYWo/2jkEr+zLnlUd6LhUE+uppIxEN6bv/HF8zohhNhkXeOf/5bd1Lg6UtffcHo8vlVhPMy4w6yz197BBNSEyy28ATpM4coDAPdES1v+Ldc4zTo//7nvF7DqiqidNWjXlGQz0THwybI3QIIoNNFBPeCVgOOzVR+sTgfqF2djYLc++OpgonnUVv71aRdEGbDLK1AubIBtATpBMqadp7TE2YTaV2GXod6QtOVSkT1xV5oiifjIylC/Bg8VEzib11ARxwjiZrKciwaphmA0jZ3/+42SqrzgV9QG8w92Ma9zHAKiIYvhbRIqifZjNxq292LT9kPL+HQclybqy9wbqrw7bgKrsNvboWkP83iFRKXjlBNEYTBBF4fqZor500kJKFCdrH8X4+nP/c73ID/VHt//sJ3tw8/e3ouQpAt8q/nw1tp7KFEXTmM4eKyqKADDEh2nhgmuCBaIx8ti2mfLXpfu+NJa0UO59XP+5TKimN2jGBVZ8iN/WuSguC1H1oaVE8fjLZtdR//74e//TmfZOrc6F6UjoJEaDKDbQQD0xchzR1FEkirJJRKWKDH1ev4Wdo3128D9nOXFqU8dhIgvsws0IY1WKosF6aqoRFGFDFCuxyzQriKJK5RInC3TSoCGKXtnHUF6wnqpQC4VtXGoUK009rZ+iuHFrLx7a3ot3uU8k7hP1dHZ7nfsoAEnqKZC0ntKFAQPZuPup/dr7e06Mau+Xql5OhrfJjxe8EnCQ9BZ96YHxr+cRaxRDIjCST36G/lHBjpcgiq8BRVF2vk1oH0VdjWJ830WZl3FFJnCv+D7wTvcJPLj9ADY+nbIcwfj5yHWlVmE2AH/NN5EZNt7JiGK/cH1g488IeayN9XT3Q8k6PvZa4tzGBrXoDXriVf72WVfFfSTZYpt4DoqKosl1VBqNv/eYZP+qsPKD5t9tivRvbBDFBhqoJ1Q9FAFFjWKlRJGcyp3zzY9vnx1MyqjHP5MBnvl+9ZMyUy0IG1Qi66ll7YjJekr3HWuPwWqz2HtkcsApS9XvYbKeApXZZSpWFFmNop31dCgfTC5KNuXnNqmnJohEsS7WU+E8mQSpp/ds3oPbc1/GP+butH7OfCf43cdswmxSKIoH+/VEMNG3UYRMWZ4INZHZuqgda+iwOVSi1lBYT0ck+3FgRFQUhYW2qR5kAyjCbCYw9dSyRvEd7mZ0OkEKsOMA/5T7N9yR+xLu/fWeGn8+8nlqsV/Eaz9gEdwS2sfzg0mL6IBIFEN7Kk1ItbGe9u+TPwYI5ja/QWr2TM6k5R+wC4Mz4bjg6DhJ1FPWakg8BzsXxfu47xWz60hMkrUBI8Gm322K9G9sEMUGGqgnVK0xAEWKZ6U1iuRUvugG8+PbZgSTsse+EG8re7WZlKlWJxmapwcT4XI4oNXMekq0HFrf4Wbj/Tp/OXDtN9TvYaMoVmKXoWSY1pGoyAsbQFPWKJ4cC/ZpyebSXgtb33goiomJom3qaf3CbJb33Y93uJuj9QgbHPADe9doQaMolovBuccpivrPPa9LT/jLCOrrlJASxQkIspks9TwqophPXhMHRoVtbi6etAOvDUVR2h5Dfp6XvDLWb96PtV/7JS77+4ex9mu/xPrN+6urDUzVRzG+7omHvO8H5PGivvsr/ywivFIQKsPwvWuqV8DFaz9gJjOzz47/Hu3n7xNVq0hRJAuWSuup5bX20HNAz+b4tulae3x3bcYKkSgOHoj/LjKiKFzf3CzQfWrwd98reteRrM+xDm4Tn4hu+t2qsd6OIxpEsYEG6gmtoljLGkVy0V12ndluefTF+k3KZJOjBRfHF938ELD5m/F9+35lN7hWknrKwFZZMzl9SwOTolipXYaSYZosaWs99exqx5ii6CMDzzeECtUkzEYY7MdDUbROPa2f9XSt81MAdoGWbL663lsDAMiXNGE2QGB1Ktm3x7h+9SL9+/vJtE4OMmV5IhTFyVLPk6hRDImiZB8mwmwAPhDstaAoiuebk5Ee+Kxu99YN27Bl7wkcGBjDlr0ncOuGbdXVBiasp+pjs+THY4D4Ednta8Jzt2owBZyqWL2bq19slVlPdWTmvHcDC1bFt0X7qagERkSRLFiqCJEtUQSAlx+O/6ahWDLUwnLp+8DxXfw2ShRViiIQ208HeoK5wbXfDkJ+GLpPCwifTdsuilPfFFtfAfPvVq31dpzQIIoNNJAWafri6IhiTfso0lPZCS58YlDNmW+N/977S/3rVTMpEy/MbTOBP/ifWFHt3wM8QGwqI8fsBtdKUk8ZGFF0c8n+iBSUxC17f7J3YKV2GVUdZkVhNjpFMd5/RvtpTYiiqCjWoz1GitRDioQSWbtJ+yLX3o7kALjfuxgbvCChT64oErJWHBVST/Wfe+2qhXj7+foaob7hgvpOmbI8EZHtk6WeRzy/w+NG7KEIKIgiTT59TSiKdoqeLlRp0/ZDldcGWhLFklfG32/aJb2PYnGKc1eLeingMkWRlVCs+nC8bcaZ8ZhEraOiVTJhPT2efJxNH8U0MC2QdKRMI5dhpA8YG+C3DR+NWwupahS9Emk/5APfeFvwW/pkf196U0D40lpDRTcV+91mnhlvczLAe75SG+vtOKFBFBtoIA1oPLJNc1ad9VTaHqPCCwdXn+cFE712gZhSsiHGaIuoZlImXpg7Q8WDEbSioqbKNLgaiaIk9TS6TYiiraJ42hXJVgKV/j4qe61RUUxXo8ispwDMgTY1ST0djzCbSlNP66cots1aor1/xGnFcMtc+IsuxZ0z/hg3Fz8Z91EslZNWUHpMFkeEvpn67+tmHPzN+y6IbrfmMli9pBtvPS8+/49riaLkfJgIRXGy1PMorKcygi8niq8xRdFyMcgUqrR+s/5+JSytpxu39uKxl80ksG32qZV9DhH1UsBlNYpAMKYvfmN8+82ficckSlBERVEcy0dPBK9tYz11nMps6CwsT4UL1+rvt4FoOwUA+HGPYpmiyOZvrz4abzu0LZi/nSSLHGy8NVlHMy6w8A3xIvXw0eRj3Cx/jfXLwKlXTBmSCDSIYgMNpEPaVcRhShRtwmxqYD1lAwxVMwE+TdA06apmUiYS4C5GFC1qEXWDq7RG0cJ6Wvbi25lcSJAUnkGutrGG0faq765UFMUaRUoc1AM3VRQ92eWdDvp1SD0tw6l9jVLFqadijWLt1B0nrC1JtMIIN/xV4YNY2v/PuKnp7/BA01sjkggEybRFT3gm+Wybnt6Dr/7PC9HtX7w6aNx/R07GCuSHLz8N9950Ga48O56sHR/Ky56WeO8IE0EUJ0s9j8J6Oiwhion2GACvKNZQxbZBfWoE7YiiKVTpgOF+9fvbnf93P7Wfu+YlahTD/51aHUf1UsCja7/EeUC3UaJBraN0Ebg4KiEvflDHyFlPNbkC7DqqIpMU7NhoagfOerv6cUsuM7+WCZQoziT9HAcPBvuJLQ7TBVHd/I3OGdh4u/wG4Iy3qD/DpTcDH30wFgFkRBFIWnEPb1e/5iREgyg20EAapF1FZIpitiXZeqKWYTYcSQqHREpSgdimkckCq4T+QyKqGUzFiWdXqL7YEEXd4Joq9VRy0Wev4Th6+2mIv/jRi7WZaAFq66l1e4z0RLEos57SxYqapJ7yq6LfeqKn9jVKCetppTWK9sTfOOEOa08SrTAc3ma6afshHBoYS7z+mFinSFac73joORzsiy1Vdz7Ra9x/B8l7zO8Mzr8Z7fH31SuKk6RGcbLU8ygVxUqsp+NHFOtWIyieb2L7kBCmUKX5hvuVsGyPcbB/FD45IxM1ikBtj6N6KeCyGkUGupDpKIgiVRRV4+lT3wRefoS9EPD8RnVGABtvZpwBzF+p/eiRmp4/CVz1+Xh707R4HgAkF7HTgJX+PPK38Tbq1hrsjdVEgD8HbXpMAyTTwAUu/gPN4wrBfmPj6tAR+ePEkLwjL8gfN0nRIIoNNJAGtquI7GLGksB8H3j63/mLcS3DbGjqKRtMhhSrW22zAstKvSZlsjhqwK5fom5wlYbZKKynHFGUNKu3KFI/MlKuzUQLCMizLOhFpTjUpEZR8n50VbgOqafDJTmhrqpGqdIwm0QfRbvvazXhzrgYfe838OnijdhcPhu9/kxsLp+NTxdv5GymAHBMouaNieoU+S1aUEATYgKSR864/w4OxGrN3M5WlLwytu2PyeaX/meXesFjsiiKrJ7nvXfUrja40s9BEZ5vw5I+iuYwm/GrUaxbjaCl9dQUqrTuYv39SiSsp/Lzf15XK6co/tI7P/r7hN+Ozzofq+1xVC8FXEcUVYqiqkaR1idS1fCnfxu00gAA+MB9H1dnBLDxplwEzr5a/bnPezfQMS/4Oz8EFAk5uvRjwFs/G98ePIiKQEt/TpLgmr2/iP8+eZAPA6PnoGn+xkAXZvteUT/uyX8N9huz2o4cl+/DhqLYQAOvI9isItKLGVvZ8vLJOkYZSag0NVKszyuX1TaI9tn1nZQlFMUwTcxCxdMOrhVbT2mz+vA1msxEsYD4tauaaAHB8rZIlDNZdVPzRI2iraIYPy5BFLMt/ErrL/6lBlHuovVUPaRUXKNUqfVUVBAtbYC2E+5dx0Zxj7cG1xU+i8vzX8F1hc/iHm9NYh8URJspJL0UycJFq5NHM+JjtuAH+1i3/6iieMr0Jtxy19P4+mPxBKe3f1S94CEjM7VoHF4J3GywiFWr2uBKkLCeBsfNCFEUm9zgN+4flSi1E6Qo1q9G0I4orl21EFcvnSu97+qlc7F2ZYUKm2WYzfWrF3FEsdefFf39ePlCPNp2VW2Po3op4KoaRYAf12wUxX7ym89frn9fVUYA2/9ekQ/ZmnNhct7AXFP5k3zQTEsn0EH6O5+sgCh6JeC+T6itowyDB/gcBHoOmuZv0XuR8/rYS/rH7riPhOD5QTgf91pFftwFgJ0/1ocgTjI0iGIDDaSBzSqibR2jVFGslCgKtsuxfn5VjKI9VJXqNSkTB/aoRtFAFE2Da5owG5X1lA3COeGzSBTGgmDdrHiixdA0nb+tIy66GkWN9ZJTFH3hdyx7/KDX93LtotxDeL56SKlZjZKt9ZQ+L5NTk3IBthPunYcMEfDsrSXlsKNi8/asoCg68e/NFix0+4/Wh23d259OWcpkkmRxIhTFyQJVH0WiAs8N7b1jxTLf7sQrAcdI7dRLm8ZtMli3GkHL1FM34+D2G1bgo1ecxm3/3HuX4o7fXQlXdiLYIJFeLD//165aiCvOii2IrU482c8jm1ycqRb1WmyVpZ4y0HGNq1EkauFIX+xo+tk/ksdbnNOyjAC2v70CT8Cu+3Zy3sDKS4rDPGFt6QSmz4tv0zYWNmCL78983/zYwQOC9ZRYnk3zN/p+DNLQHAEnSAsS0X4qaxVSLulDECcZGkSxgQbSwGYV0baOsabWUyH1VOWVB8yJZNVCmXpKiBKt11x4id3gWmnqqcy2KSqKVAUIURSIVsUTLQZRUdTVzFVsPdUoiqqFg2qi3AW1wdMkrVZco1QLRTGFsmOacPeeGMH6zfvxhQdetHo92QR5TCSKZKEisJ4SRTFcsNDtvwOhoticzeC+bfpJmHTBo0EUY1gQxXmd8f6K7KdsMrt9Q/zcocPjNhmsW41gwnqqnjZm3QwuP2MWt+0Np82snCQCEuup/BrjZhz8zW8vi263IL5mFvwcDp8ck/cwJUgdBlSPxVZHQxTpuEbHu6b2mNCP9sWOpkGyKLTrQfN7y2oaI0VRIIqy0oVmMsZTMigSxbSKom7xXcTgAcF6Sq79uvkbRRpFEQAKw/HfYjaEWJ8ootq+1eOA1/Fo0EADFYCtIn5lJdC/N9jW0g28/W8Dkphx7esY6xZmU05erCjqThTJhbm5M67ZoYoiq49o6QI++pDd65qsp6owG1kQDFUQc21SIiGGwVQ80WIQw3y0iqJIFO2sp0N5GmaTYrKy9XvBBCctREVRs/ZYcY1SLWoULYliySsjl9Wvn46Wyrh1wzar17t66Vw8sjN5LiYURRIo0+rwNYpMUdTtP1ajOL+rVRqeQyFd8Mi1Bi4Ehtc1UZTXKFLrKb0WDI4Wccr0FqOTxHvmB9hQXoO7n9qPg/2jmNfViutXL8LaVQurI1Ihrl+9CFv2nlDeX7saQf2xURJIVc+JEZwzd7ri0eFzvDI2bu2V75uE9VR9/mez8X3dTSUgPM2KyML3gd4Tozh9trxWntUmUzWe1Sc/svMIbr9hBbLuOGgrtmE29Dh1nKBOcegwcPyVoG1XJZBlBFDrKVXqZHX+lChS0tnSESzctc0KrJlpiaJtCA0Q1C5yiiIZP9j8bdsPgZ/dRuZwXfz1jxHF0RPqEh6KabOBfGi1FbMhZIqiiErH33HC63g0aKCBCuFm+RWrU87jT/LOBfoLNbsY18t6WhYUxUyOJ0vt/IpvzUEvzKw+EZBbT8XekjrU0npKSVuuTUpCaY0iUMVEi6EiRTFdH8VBU5iNChVHudsRxapqlITzpAQXGzfvN0+46fMsQkXYRHHv8RHt4/pHFMosgPYmN2qj8Oe/dS4+fNlpOPt/3594XFJRFMNs+BpF3f4rl/2IHM7rbEG+VI4URhmkCx5i8ulE1ShOMEpeGceGSqCVdj9/ZRCXn+Nz7TGkiqJhMvvqQ/+KW/tji2CtScjaVQvxyM4jUttxVeefZZgNg1fmLZ49J/QKvYmg3XFJlr+Sad6/5DvRpHa6GxNFpsrv1xBFm9rkdYbAnpog+n5+kDWQUThlxDyD1u6AKOoWiU2QZQRw1lNybZRdU+kYxxHF0LHTMS8gimnDbGxDaACgfx/wnx+Lb4vKJ1OB208B7rou2EZJIhCQdK8EPP4Vu/c8553AL78U/C0muuYNiiJQXd/qcUDDetpAA2nhFfmLAS3aBuzT0GSKUrV1DUBAkihRnLOUf2w9FUWvBBzZEd8e2B/X6DRLVpXF3pI6pEo9pdZTSfNyaj1tapP+FrRGsaqJVvQ+aRRFwX5UQXsMnQ00gWqj3EOIQS4OgH9ce2F1NUoCUbztoVftWgDQY8OiJ6ZuosjQ1aonULQlxaLuNhQVSbk66+nyuU2covin77pIu/+ODxeivoxzO1sqS58UW6W8DhVFRlh2HOYXCr72eA9u/v5WjBC1fq6MKBoms62j8slx1UFZIViN4O9cwv++H7h0cXXnn3gsGALXxB6hPSf0Cy8mgvbYq4P8Rs3178hI/BtNyyRV+X196s9StzCgtBDLSChUiiIQ1yl66oUsLVQZAZz1VKHUMdCSEmp7ZUSR2U+Hj6b7nLYhNAxHyRwko7j2zzxD/fxSPrDv/uKL5vc6793ARdfHt0UF0kZRrKZv9TigQRQbaCAtTh4C12pbJIq2aWi1rFGkTaNE6+n8Ffxj60UUWY3Oiz+Jt431xzU6soEllaJoSj1V9VE0tMfItUknH0VksbC7Fbddu6y6iRaDSJR1dkid9VQbZhM8znFSWk8rjnLn36OEDDpasuhuCz6jD+Cdy+ZXt++E4+axV/qlD0tMuFMqiqaJ4uIZrWht0u/TUaI6PdPTzxHCJqIYJYI1yOeb2wY0OfGE96pli7T7j7bGmN/ZWln6pLjqPp4po5MEjLCISnzRd7Fp+6FIaW7NudhxMCYvn7vvBazfvB9+h34ye8BXNyyvFQnJuhmsWNzNbZvR3lzjGkGTosgTxV5Dza/pvHvoRcFOqzk2Dw7E1/pWJ3b9sDKCHg1RrFsYUFrQ/SvaT1WKolcitXApev46GXMAT6QoFoFSuA+yLfJaVaX1tCv5Ge98q33Qk20IjQyHFWUCnYvUix59L+trIlu7+f1G6y/FfIiCBVGspm/1OKBBFBtoIC3ExC7RtsB88N0s/c0BFr4heTGuKVEUradkVWu8iKIp7bXn18ntqYiiZN8oFUUyWHLtMcJBj9pgFTWKBT+L37lkMdat1k/UrSEqirZE0SsBJ/bE933rauUAy2oUZ01rRskX9hdNxqOoRZR7iDIy+OK65biGkBHTBMwIYT/pCPDdv94XhVFcd+eWaLuvs/mGMH3OsWLZWKe6qLs1Sjn91i/24EPfio/57vZ4wp2sUSQLF8URznp6aFg/8TvQH6/yz+tqiZSl265dhk6igP79NRplV6w3sk2WfQ2BERZRiWdq1KHBYD97ZR8/+HVMbvYcH8GtG7bhe/krta+/3lujvO/Znn678BQL5Ev8IsRLlum8SiSsp/pFhGSNov68Mp13h4aEc0VzbPYOxuSwmYTZ5MNr4Xd+uUe5j+sWBpQWOqIoSz1lC7SH7OqmOZy+xhzAIwuzUS280TGOuq6yrcFn3PvLeNvBZ+yDnnSL763d8u0M+zfLt2eb4kR2EXS8lWHWOfx+a+mKfzfR+mtSFKvtWz0OaBDFBhpIi0HBJlQYSl7o3GychDVnKfDRB5MX40wWgTkPwrYKINouo4u0k7Se/uRT9YlsNxWc7344uS2N9dSoKJJ9WVZYTzMSRbGpXakoFko1jFQXaxRtwmzgBwPp0Z3xfYpYbd/3I+vpnI5mlMTL+7xldYhyT9YonjarnZtUmRQFI1yRKKrPke0HBnHrhm14Zu8xXDT8eLR97NBL8LZ8V3vMmyaKM9ubjLbOou+DzT8LXhnP98bKU1drfPzqahSd0ihnPT1wUv6ZWULj53/yQrRt9+EheGUfWTeDdasX4S3nxufXOy6Yq17waKSeRoRFPG/Y8cauBQWFnfiz+y9Cz9y3Su/7VfPl2OCpiWTR8/U26hQQr1kvHak1UTSE2XjpahRN513XdEn/WQV6+2NymCvHpJGR/bFSWbmPK7Js1wPWimJ4nKZJBBVJ1ZwLzM9h4265FBNFsaaZQVZekmsHnr/XrmUYBWvx8Y2rgH9ZBpw8DCxYHd8/69xg/JKF6lCMqgOeMON0+faiYcwSawozmaDmEZCE2ZAaxUtujP9unVmbvtXjgAZRbGDqgV5A/nnp+DculfUAygt1FIXheGWp+1T56zhOUlUy1H8oIdYosvdumxmke1EcfLY+ke2mgnPxAgpUZz11MgLxTmE9FRVFCWkrIqusMasICUVRF2ZDvsvOH8sfIwywo0UvWiWf29GSDLPJttY+yl1UFB0Xi2e0YUFXTDyo4lUR3Cx3XiT6QxKMlcpw4eH23Jfxv3N3Rdtb/VG4931ce8ybJoqXnjFTa+u8YH4HRwxF0GNJpyg6pbFIUfR8BwdOJj8vq6e7dcM27CcT8W/9cg83AZ7eEv8+tH41AVngw+sMjLAkFcVgX5hMBWVk8KnyH0oXY/b/5lcT9bs6VFO3KBLFPceGkwsTaWDZnoJBVBT7hgtcYqwI03n31gsFq7Tm2Owl1lPXi88L2eKSuI8rsmzXAxnBHUQhq1G0TQRd/gFg8Rv5bXMvND+P/v5j4fVNSRQlQUEtnfYtwxiYSvqjW4KF0cGewJHU+1T8mHf9czB+mer7dOF9MxR1iqaadtl7TgudWokwG7JQc+F18d8LV9emb/U4oEEUG5hakF1AxrtxqYwoivbTE3vjv1VEEUgSxZqlnoakzG0Cdj0gf06t+/eYCs5lNTypwmzk/c0iqFJPOespC7MhRLFJlXpaa0VRWG21UhQNIAMsJQIz25tREidHFvbL1BCO1662FjRlM1jQFROfmtT2EMVLpygCwDXuY3iHu5lzHwNh5Y7mmF+7aiHeeLq6juzcuR2RrfNNZ8aTj6XzO3DbtcvQlNWfu30j8SQ2UaNIJl6uNxYpigXkcGgguf9sEhoBoKMlnuRFoSsyNMJsIsIiKor5UI1yNf0DGXoGCtLFmGtWL1GSEBUqrVsUewWWfeC3v/p45ZZW0eqZskYRCNpSqLB21UK8/Xz5guHVS+fiqgsX8xs177+vP1YRM6RFQkFxzaD7mJ3bH7psCfcY13Fw5GQeG7b0VGUJtgZHFC0URdtE0OO7g4VjCtFtJAMdG1keg3i9YKBhNgwtnfYtwxhsVFJ2zTTVL551lfo+laJompeINYVeKXYujRwD7nxbLF7QGsXmjnhRsKgPeZpMaBDFBqYWTHVw49G4VLSeAslAG+px1xFFkSzUwnpa9mJF0dTsVVzJqwamC/aKG5LbqlEUtUSRWk+JopjJBhf1g8/G2159TNrXqYCc0mZWEVIpEYyptgAAIABJREFUipbHARlgKVGc3pKFL76GRaBLagjvMXN6MHjP5xTF6ohiyStHk3UAaGuVf4+WXPD7X+8+CoB3IgPE5K045t2MgzXnxPW73a05nDMn/s1OhEQv62a4dNO7Pnop1q1eJCV0FAVCDhMKD5l4uaWxKMymgKy01YVtQqO9oigSxddfjSJTlDxBsS6G7UlsSIKqjo2RkIXd9nVulZ43MvVwx8GTlVtaxcVLw2KmzIWhs5+6GQd/8c7zuG1nzG6PQ8RyYh9F9bWxhxBFioIvP57FfZx1M1i5iLdner6PrfuqtwRbg7OeahRFtjhsmwjavz8M4oteIKjfMzmx6DjLnFOiA4FBZj1t6TR/RlGhs1FJGeHS1S8CwNlXq+9TEcXWbrtAQiAWL47EJQDo+XUsXowRl0nz9HiR2jQ3m0RoEMUGphbSWhjqAamiWCFRTCiKFRJFutr9/L3xSqTpYlTL/j2mtNeVH0pur4ooCoO/MvWU1ii6wUX9me/H20aOAQe2ci/l+Q7KyNTWelpRjaIBZIBliacAML0ll3wNy6bzqSASxY5g8J7R3oTmsHF9NTWKzGJ5ohCzvuOj/IS9qzWH265dhvPnBRHs85zj+hfVHPOb9/RFfz/wqStxx++ujN93KJ6EHgv/zrkOOlqDfWCqteogwTKJybybjciZW+YVRVnYh21CI33PwTGNopggiq8/RZGRuXndfL/Xz7znIvzL+5dbLRrp6tiybgbDeXvHS6XhKdsPqO3PFVlaq0w9BYC/+tF2raLZIxzPf3zVOXGImHjdVyxi+H5Q5+n5SY+wyoUg28ePv3xM+ligdq1MtLCtUWSE3TYRtDQK7H6IbPCBH3/S7MTifv/w91PVBYqLoQDQ0mHfMozBRiVl1ywWHvjeO+SlO02aGsauJfLtXjF4zVx4LXCb1DX9JvHi8Pb4dvO0eN8VGopiAw3UB2ktDDJUW+OYliiqLkaAZBCsgfX0yX+N//YNk5ta9u+hF2xZYEq2ibesOC7QpkjilEGsTanEetrzlFXhP5tYiAmCVSFV6qnlcUAGWFFRTFp1g/djIShrv/bL6pMWhfeY1RkMrI7jYEE4CTtgUNp02Li1Fw9t70WOpIB+p+kfcJ37KDIIfpsLF3Zi3epFeH84ST+oaUMAQHrMl7wyfvjrffjpzsCy3ZzN4NGdR9HVFh9jx4cKib9ntjfDCaVLU63VqiWxUkHbaEQIJz5ZLx/VKOaRw0GJomib0EgVxUGt9bTRHgMIyFx7K39evm/Vadx1YF6nXE0x1bH5vo9+3W8goNLwlJ2GlNPUllaRmBnq6GWEel/fiFaRY61HGLg6R4sayZJXxrce34OxYhmeZFqbVxBF2T7+1St9kkfGqHs/RW3qKdkv7HcwKWoMo4rvZXJiyer1VO4UlaJo2zKMwUYlpWTVzQaW7xmnJR+n+qxeCXjkc/L7+l7hby+8RF3TbxIvju+O/26aFs8DWNjhFMDrb9mwgamNzgVBXaLyfgPxYTYBShZYneOuBwKiowtyKHtSm2KCKPaTGsUuocaColaKolPhmk+t+/ewC/aKD8jvb2qPezG1z043ITUqitR+qwiz2RsnYepQEJIOa4JEjaKl9fTMtwkrwSGEATZBFN0cQPlItjlS6Gh9G0tbfGTnEdx+wwpkXbtjqeSV8fKRUZxDth09WYRX9uFmHMzvasUrx4ZxaGAs2mbzmhu39uLup/bjYP8ohsbGcHvuy5jpxJPflZndWJnZjbdknsYtxU9E6tzaVQvx0AuHcfdLa7A68xJ8X2gvitB+Khzzsn2SL5Vx64ZtePv5c+BmHHhlH8eG4+OIKYqzpse/4dpVC/HIziPK2sErz56FnzwXXDvGZMdVrhXIDyLnj8FliqKflRLF61cvwpa96jQ/NgGmNYqprKeapuavdRTKwvHvNmGE7LvfOHs2Vi7pxp9t2IayD0xvzuL/vPt8rF25UHuMD+VLibpZFaoJTxnW/c6owNKaUlHc1jOgvI8pcuuERZU9x/lJM0cmReeF8HnE89dHBvyFL049pVDt435SSyxD3fspWofZZOLHX/tt4Nm7gIf+Sk4IW7v16Z9bv6ces2XjVFrrKfuMj30RePTvgu3T5wNv+YtgDBPnASs+GMzJdJAF6kybwxMzQE0Un/0B8OJ/y+/LDwbk2QtdJLoyEZN4wWoRc+3B92QKZ3HqEMWGotjA1EJaC4OIamsch47EF2uqFKoUxenz1RdVoHZhNpU8byL691D75bSU/RzFlW2dGquyno5oBksCNrGob+qppfX0t74QhwRkckoLzFCeWk+zyQletsU6BMUENjl7ZBc/KXlgx9FINWB1ikXPx9GT8toh2WveumEbtuw9gQMDY3h76VF5MI0PvMPdjGvcx6JgGDfj4P/7jdOxwbsS93sXy2sUJce8bp888MJhtDUF+5hZT0teOQqmmTUt/g1p/0L2HIpuok7qFMWmcp7UKOYwMFpMpEbaJjRyimLDemqFvIwokt9rWnMW61YvinpUnjlnmlWvVapIq5BzHdx27TJ86f3LsWFLT0Wqf4vk2KNIbWlNWaP4fK+aKAJyRW7vMUFR9KiiqK9RFM9fmaL47hXxWD23oyWuf5T8ZtNb9Iskde+naN0eg/wObhZY+XvAn+ySO3pMLSR0TizZopHq9TJu8r6WzvgzLn1fvP3CterUTxuVVEoUJSE0qnHWpARu+W68/3VlIib1k/2ejERHNYoNothAA/VBWguDiGpqHL0SsPnO+DYlh6P98WO2fBc49mJwu3BSb2utR5iNCm5T7frnVQpKltLUJwLJASthPaXykUJRbDfYEkOIvdNqgia+9slaUYQf3553kdQCU/LKeOyluLbmiw++hLGSMAnKNluHoJjAJmdiCw4PGWzafgj3bOnBieGYmHzoW782TnRlhE0ZTBPeXuc+yrWaePHwEMrI4JbiJ/Dp4o3YXD4bxfb52mPetE/YpJVN9E+MFCPiOrOdP39Z/8IrzkpGsk9rySIbTkzFZMrgycHEJ+fn0RwqisVw/4otRhgpvfXqWM89ZXpzYgJMaxS1imIi9XRqKYq1tFPnvfhgKyILOA5G8vHvxRYBmsOU27yYYKvAsSHzYsnS+Z24ZsUCfPKHz3ALJmn6Ky40EJnUltZE6ql+zDipW5CAXJFLKIr0d8u44PoNC59HPH9lRPHNF8TK4RVnzdIS+/PmSVQxgrr3U+QSzEXrqaRGkYI5esQWSCanle7+NNZTIKkqMqII8OOari6SKZB0zFywOmh0DwTzJtn3l80pVAmtacqYdA4Lk3jBFES2SM7qHr0Cv4g9idEgig1MLWRc4J3/wm+bPs+e+FRa48gsq499Id5GW2KMnogfc9/H41qC/El96w7R0lAxUbQgfPNX1q5/XqWgF/7URNFgPQViwkwHVDrYnvk2q7cqhsmHNU09FQdQW0Wx7MUDimTQZkrcj5+LLdG7jgzh2Kjw2bPN1iEoJrDJmTgpYz3o/nHTTjy0I+4n9eIhc+qijLCZgmnmO8e5YJgXDg5En+Mebw2uK3wWD7/jEe0xb9onjGwcH87D931uwk+tp9zn7kxOTlpzLlpzwfvrFMVm5LkwGwC46d+3JIhP1s3gsjNiQnrDGxYnJsDWNYqi62EK1SjKlOhqGtePlQWiCHCKbltzsK05TNkdk5F+CWyIImDf+kSFUzrU15WKLK0praftzfr7RUXO931JjSL5zRwh0EY4NsXzt4wkAexonxb1wTxq+B1Ony0JZAkxLv0UK1EUTajGiSW1nmoWI8RxjrbMoMdS2UCShP65uP57MelUBdRIiaLifDC285pnfg3ALF6w41FUFIEpoyo2iGIDUw9MrWM4/732xCdtTDODqa/PoW2V2VpFRTHNxZ/C5rvXuh4xLbwSHxX98iPpQoRMiiIQ7z+V9fTst1sV/rMJYrFUw75ZgvXUyzSpVRCxlxZTRSXkWDWxLApqH9xm6xAUE9jkrCS0EmDEsX9EPgnQTXSl6Z6GYJoD/kwcOZmPyMCOg8kgD3ESKsK0T6Y1B9+x6PkYHCtxE/7Z0+QTiDkdyRX3lpyL5pAoSskFI4p+AbmwxorF+u86MiQlPrSeqqs1eWzY1ygKE69xrlGsRhGslZ2aYYwoioyoU+spUxRbUiqKR4n19EOXLcHqJd2Y39mC1Uu6o4CcnhMjVav+LHgn4wBnnxJfc65eOldpt9RCJIYGonjWKekUuaMn85wrAAjONQ70Wi8cm+L5K1MU3VxT1NLGZIOnLpJTpsfn9/+6/NTK9l9a0P0rhtGZFEUVqnFiSa2nmmumWGLBKYqUKFokANOxuzAMFMMxQmV9lRJFhfppIs8XXhv/rU0oD9XPK2+Nt3UsjMWLfDgmsf1CSe4U6aXYIIoNTD3QfjUAUEqGPShR6cqaybJ6bFdlttZa1SiK3jwRE1GPSMHU1qM74m0nD+rVVhGmPooAURQV1tNsizyZ9fz3cS/DJoj5WiqK2SbuM//ntqNKFaQMgSiy1VfJoK2aWHoiUcw2G5M5bW1VbHImvodskiZCNdGVEba7vTUAIK1RBID13hoUSmXcs6UHP/z1Pjy9L6hBzZLJ3N6+YDBWkZHrVukVgvPnxxOd40N5rtZsloIoypIxW3IuWpuC/ZNQFL1SFDbRjjG0IpjMFoT9KxKfAaISdrYlj42WnIumMJxIW6OYSD2tfY2iav/ni15VimCt7NQMPFFkiiIliryiaJuMfIwQlPctX4B7b7oMv/zMb+Lemy7DsoXBMXZsqIDeE/rJo0n1Z0SnJefiux95Q/y5m93KSE5KonjarHblfaIix9JKRSQs//S6J7y/eE2TXoPc5uhcNSm79Pf82Jozor/PnjO9/iQRSC4SUnBKa4q5gimRXDfvkFpPUyiKlVhPGcoiUWTBMIr3F4mi46rDCXXkOeMC574zvq0LswGC97hgbXz7gmsC8cIvx/NTpqxSIj1FFMXXb8V6A1MXtC8NABRTEMXlNwTppjLlT0embJKtKrG11iz1lFzo208Bho8Ef8+9CHjDjfJksfGEjdqqSl1jSCiKGutpWWE9dbPyZNZffQ144T+jm3VJPQWCQSJMpXvusPy43bT9EHZkRrCUbfDyMfGVDNoq62RCUcy2YO0ydTJnGlsVS90sidZT30wUn+3px2V//zDmdbXi+tWLsHZVkBYpS/Lc4F2Jt2Sexjvczdx2xwHu9y7GBu9KAIHVlaqYtMZp77Fhbdrr28+fg1OmN+OIRGm4eulcLF3QgV/sDuo/jw8XuInmzGnyCcRcCVFszbmRCjVGVSihYTNd8zk3sx8uPI6Qr9+8P0qNpESxq1X+WTpaszg2VDAoivUlirr9/90n9uB5Re8/VUomRa3s1AyjlCj6wX4YJtbT9qhGkRHF9NZTcYFhUXesMsxob8ahQTWZMan+zC7flM1gTkcz2ptcDBc8vHK0wkmpLl1aAo+s6pw6sw17QkX/ty6ci6/8TqzIyY4Jho1be/AHV5wWJzCT654HFxs274+Sked2tmBBVwt6wzrespQo5jB7ejN2HjqJY0MFlMs+MgrSlyfq5hJCemXpw3WBtj2GJPXUFqZEcuXzUiqKCaLYxX8GBpP11Pf5729FFIUwG93nZOR52w+DRfyBniDtND8IOFlh3mAgiuJ7MeUzTxwuUY0iURSnCFFsKIoNTD2IRDGNosguDvRiaVPjaLKsOm5lttZ6hNnQ17j+exNXj0hRTYgQgyn1FIi/J5WgqKKoCukQXiuyntZQUSx5ZQwhHkzyksh2hucOkAGkSCa6kkFbZZ0UbaHINkUhKBcu6OTu+uibTktlq2KpmzJFUWaBpCh6vlQ1kiV5isE0vf5MbC6fjU8Xb8TNxU9Gk0KV1RUAdhw6aUw2ZXa0bMbBvNAOyIJhqL30+FCeq3FKpyhm0NrEiCKZ8GkWUeY4/bjGfYzbRokP/d4yRRGIUxz1imJ9U091+19FEhl0imDJKyOX1U9j0qZUjpIQqLyfRbnscwpwaw3CbGZP54+bxTPjyePFp3ZDB5Pqzxa3mtwMHMeJau5ePjoE37Y/B4VGUZSpxLsOx5PjL1x3UfR3d1sTd33RHRO7jgzxlmFyfb79Z3s49Xnrvv6IJAKQK23Z5ug89sr6fpZUUTx1ZkwUD00GolhpjWI1qLZGUWU9NQW5iN+9OEKIoqX1VFdbCCTDfxZdEn62AlAi8wYroiixlBaG4m1TuEaxoSg2MHXglYBn7gJ6n+K3pyGKQHBxWHgJ8PS/B7fPfZd5lc3U1yfbbH6MzNaaCLOpQY0i9b3r0snGE5WGCFFY1SjKrKdkQFJd8IXtTEmolaLIVs8/OZzBeRn2HmpCdWKMvC8lihKiq+qpVxIv7+GxkHUzyLo8IZzb2cJN4sR+hqL6xwjn43fdD7wcv85NbzkbxzrPxZ9tfE753URQ1ehL71+O8/5yE5gg2ORm8FsXzsc9z6zBPaENNS36hgv44eZ92sew6XOp7OOihV1cP8mZhAweGypYWU9VNYqxokgmfIZFlHXuo9x3p8SHs54qCHpHGGiTqo9ijYmiyR6qg6gIsmPzh5v34YUDg/KelARpUypHPYCtfxSQxVjJ4xVFFmYTEtSCV9YqVAzHwuNmWnMWLTn+Or9oRhv399VL51as+jOiw6yxp89ux3O9Azg5VsKxoQK623LaczsBBVFUqcQUi2e2RX1I9/Xxlloby3CkJJNx8ld71QsLrTkXp3S2AQNCCJbbzJHzoyfzUc2iCKoQL+hqRcYByj5wcHASEMVKaxSrQdWppxWG2YhEsjBEahQVRLV9VjAHYON/2vlP9F19YR5lIJyAvPaQKopRjWJ78nGTHA2i2MDUALNnyVbee58K7ld50WWgFynPIo1OZ1kFgotaJbbWmimKKqJocYEbD3QuAAY1ZNAU3w0EAyMdBKTW03CiQwdUjigqyJmwn2rdHoOtnn+0KR7gCprLb3trC8AWIzlFMTlor121EP/xdC+eeIWfHIm2UPodDwsTupdCFaDklXHPUz34xwd4KydTAB/ZeSQiUVk3g9PndHFE8a3nz4M3bxEeffGoUi2QgU0Kj5zMg+aX+PCxsNvQA8wCPX32A7Jod6T20j5iPXUcoFuh4rXkXMxob0LfcEwqm7OZqMfdaNGD7/twHMe4iDJfSH6lxIf+Rioll7XIGBwtxu8pQpx41TjMxmQP1YESY51dUYa0KZX5kodCOUOIYg4nx4rYsideiPnL/3oev3/ZaRFRBAKy2GKYuLPjZpbErkytpz0nRnH7DSu475nNOPi7ay7E2pUKMkdAFUUAOIOkeP7bz1/B+i37pef2d5/Yg6ZsBocGxnjymEg9Db6nThFkaMm5mN/Vgv19owmimMoyTK57CacEQcHzMFz0kcgtdXPcos7Rk3mcM1ceusNs4U1uBk3ZDE6Z3oJDg2M42D+K9cTyaiTYlYKrURRszROiKFYZZkNTT7kwG4NlWySSY4OxO0jXx7F9NjAUJm6nnf/Q+RdV+2wUxazMempSFMn9kxgN62kDUwO6GrfhY/JEUR3oRapkboQcWVanzw9uO25QDD53WXC7OBwQmGu/DZxyfvy8hW/Q21rFC5lNP0QZ6PPE8JbJgGriuSm49DvL1NOyBVEUFUVGFGtkPWWr58N+/HsUNNbTZYtJ2qfBeupmHKxbHU+Gu1qzWL2kGxcuFnr5hYsS5bKfqMd76fBQNAn/zH88Z51aSoM/AACZLNd43tTTjYFNCvcLk8mi50ckthoM5C2CEwio3XFWO289ZRP+GW1NcQ2VBFRVbMkFNsCWkFyUfZLsaLCsHyDJryLxGRiNz3WVoshaZJTKPl8bSVHnMBtTsqwOlBjbkBOGU2e2cXZqm2TVkbzH9QYtIotb73kOD+88Em3bcTBo9fLM/rg9ko39lIXZyFToeaSlxQ9+vQ/Xf/1XnFrcnM1oe/9RREQxVK9Pnx1PTL/+2CvKc/v5A4PYuq8/aQsXp4nhsWGjEmczDhaHamnviVEumChVAjO57om9Wym8MnBkSNaGilcUdYE2TFFkiiyrN3712HDNWrBooQuzoeNapXOFtKjGeppt4eufM5n4cxutpwKRHIn7BGvfn9Yp6kJ3ZKDfNS1RzGTi92PP5WoUw/3C1ShODUWxQRQbmBqoRY0bBb0A2yiKQKBYsgnUrLMDX/ucC+L7xwaDx7AVo9Zu4KMP6msEKVHMZM3ppSrYvP5Eopp4bgpNTDoAs/U0bY1iSkVRNRk90D8KFx66nHjg+JPs3bjOfRQZ8O9x9dK5AlEkg4liwNpDWkD824cuxr03XYYz5nbxDwrJwPHhAt/QGsCuwyexYUuP1SSckqgEUQyJOms8/3/efT5swCaFeyXK33ZSw/Z3v31B1FZgjqZfnAjbOjIGqmZ0tsak6e6n9uPFQ8FvqAqyYaB1iqx/IqtvAxC3BDAsoqz31sBxENVMUrLAyMS05qyStNIWGco6RXGFvsZE0ZS2e+Yp8qRMkRinsbAWSuVEcIppoj+UL3F1twU/i5/tOip9fWqzNPVSHC14GA7rHEWiWPLK+ON7tkW386Uytuw9wTkEbJNVg8eGRCdclFgyo3JFftP2Q3h0dz+/MRxrbFTibCaDxTOC37ZU9rlAmFQJzFRR1BBFQBVm05SwnqoQWXdDoj03XPARr5kMlbRg0cK2RnHSWk+JgtjSmbyffb+01tNhW6JI6hTTzn84okjUPtvXYZ+LLe4WJERxCqaeNohiA1MDtahxo6AX4JIlUQSCRCwgTrCiF8KxcEAd7U/ep4IrEMVKIVtddJsrJ561RjXx3NzrkH2kq1Esq6ynisl9Vq4opmmPoZuMFvIF3J77Mi7KvBo9/qzMAfxT7uu4I/cluGHfvM++53zc8bsrkaE2alqDq1BEXz0WDzinh0l9uZzwXcPB7rCk1ma44OHLj+yy+p6URI0mFEX+d9RF5VOwSaFoTwOA3vD9OltzuOENS6K2AnSi+cbT9f0W04IR15JXxq33xpP4sWI5UgKPDRW0SgJNPmU1aaxGESDpippFlEO5hdjgXQnfB95z0fyEosTUIZWaCMSKIgCcVBLF+iqKsqAihquXzsXNa87ktp0yvVlKjNNYWKkiZeq1+JmNz8Er+xgulDgFrWhZnWNaiOAST6fz56WNSloq+9aqVawoZlDyyvjyw7utnqfCgzuEer/w2LBRiamiCPDnt+6YWNTdyluGUxDFRHsMxwUyLm891SiKrH6YEW1ZgrEImxYs1r1COaIoLEDQkopxUxRl1lPN4kMzIUJSohi+nqk9hkgkh8mCje79OaKYtkaRWk8JUbRRFOnnkqWeSvsoNohiAw3UDpUkiupQCVH0/fjiwVaHWolqw4ji2EDwf4ug6MhACUo1NQeyQWOy2E4ZxISxjzyQPpHVZD3NmKyniomfqCiSMBvbpEDdhO8txUfwDneztB/gO9zNUbLlm885JZgY08kCpyjqiWJHSxYz2ptQ8srYfYwnhPfv6INX9rn0vpkk0IFLDtSATsBl1lMKOklsVqRTUtVo33G1FUes7WomgSCf/M0zMb+rdsc7I64bt/bigRcOSx/TN1zQKgnzOniiWPLK6CE98v7XtzcHE0VkokWUofZTuddo8fNY6/4cGZSlaiBLb+xS1EoCvKI4MKqYnKVIPbWe8BIwOzL9jeZ3tURkcPdRvlbn0tNnSq2WaSysVJEyKZH3bOnBzd/fisHRIjxSA6ezh1MMF4rafaJLyrVVSW1VRWaXb85msHFrLx7aIT9+bXHopHDcheOUSRF0AGQyDpaQRNe95Px2Mw6+8jvLwX5h2vv0sjNn8b89ue5deqbQAkFAQlEMF8g466mNohhaT2UJxiJMLVhsFW0ABqIYPs7JjN8isNR6qtgnXgno3RrfHuwN3F70e7j1VhTJ8aH6nCpUYz0FYhLICKCxRrFBFBtooHaoVY0bA6c4WdQoAoGywwgmO+k5RXEgmPmPpVAUKZmrZhXfpv7xtYCKrKc27THkYTaA2nIkQjfhu959NPh4olMzvL0uvD+qS+KIoj7Mxvd9vBJOtE+bPQ1e2cctdz2NB3f2cY/7/EOv4ubvb+UmNceHLY99AjoBH0lYT/khpSXnYn440RInui25TEI1kimKDGJLAZocWSz7UXhHtcESlLiaJvFf/eluKUEqeWWu3vLQwCjed8fjePzlWJ15/sAgXwe27P0YEIhiV+lopDoPDvNE3vf96HipWlF0c/xvp1iQSDXhFZB1M1y/8PctXxCRQWbnZVDVkJnICcPM9iZOkbJRIjdtP4QHtx/mFMW8paL4uft2KPfJWKGE/yILChu39nIk0lYltSGK5bIfKd5NbqaqtFmGGR2CKyC8Nq1dtRBvOnOW5BkBcuH5qFIUAaDg+VHi8BtOnxFt584prwQMxWrSXxb+BZ+c8auEZT96uDitDa+ZXa25iIzqFEWmDjP130ZRNLVgMSna3IKTrkaRzVvGK8gGUFhPJd+XBQ5u/rd4W2EY+NEtwD0fihVENgaL302EeD9HFBWKolcC+skx37s1SVR1oPMDSuIqtZ5KaxQpUWzUKDbQQO2gq3Fzc/Y1bgycomgZe83ZCBREsTgaE5NWC0WRXoSrqTmYCopiLeBaWk85omjROFeYGNNEUtvkU92Eb56QXCmCJVtGQRMqoighukdP5qP6p9NntUeTEtGiVfBz2LT9EH72krzmygZizVgiF0NyDC+ZKbefuo6D64TEQDaRlEXXi0pMKyGKY8UyhvK8ZcwGtM7xwgUdCeJqmsTvOT6SIEiMTK3fEtvhR4tlbVP5z2x8DuVn7sKCI48qVefs83dz28eK5ejY1CqKrbRGUTE5cxx+8qW4FqWa8ArwfR/Hh+MJ+ihpEbLTkiiuXbUQlylsxi25TEQEFnS3cseVrRL5051HuBpFW+upmDjMsGn7IVz7/z+B7zyxN9q2r28PhPvyAAAgAElEQVSEI9a2ny1vqIME+PCt5lymqrRZhqsuFMh5eGy4GQcfveI07q6Vi7ui8By2/xcQFfnbj7/Kqa20ZUtXG7GXsu/ByMeRuHeyc/g5/NHIlznLPoWKKGYyTlRXrKtRHBPDbCStbkSYWrDYtAKJYNMeYzz7ItumnuoCB3fcFwcOstczhdmI95vCbNix8vy98bax/iRR1YF+Vzrfs7aehmMdI4DSGsVG6mkDDdQHrMatI7SgOpm4aDqTS3/hpBchW+upbHVIJIrMdirep4IYZlMpFE2GX3PgiLWlokgtLorjpOTwr1UkljMbomhq/H3Q19fQsWRLs6KY/M6vCPWJbFJSFIhiPvxONK0xDT7/20sTNWMjJXmYDcWpijrF4YLHEZeBkWL0/VcuTjYdF4liSy7e36NFDyNhrzubiR3D4cHg3J/eksWPbnlTwu5oM4kXCVKaZE6Ge7b0YNt9twNQq85dO3mi2M8lnqonMtOJ9VSpKAKCuyF5nJW8Mu54VF/vpqvVGhgtxkmviGvBhvIl9JzgCc2xoaTSXfLK2LClB3uOx8d7W87FqiXduO3aZdj+11dH6tWA0FDdVok8PlzgFlgKfg7LFsiv4+fOkbdXEPF8r3qBYOPTvdafzSaQiaqOTW7G6vjNOMAF8zuk9129dC7efqFQ9kGuTWK98/c/emlkZ89mHJS8Mj6z8fno/rEwqIcR5f6R+HemrWaKTFFUkA+2ePKB1ieS90E4gcg4yFwJ2tTTYmzdBYB5nfp9aNOCJVUrEG2YDbOeTrCiKCNqtoGDUZhNjWsUbYmqDm6tFMWR4CA11ig2FMUGJhu8UnCyfuMq4J+XBv+nkeUnGm42PpFnnAEsuiT4uzSGxDK8CVzqqaX9TiSKXgnY92S87edf4NNXbWoUa6Uoyp6b1p8/FUAv5Drrqcxa7DZJ6zpKXhl/cz8/AS6QyeJoQT+gMQVpr6a+7u6wYXpCLQr/Xx/eHxNF2hdT0U8srBWjYSuHBseiSYenIIpDKVtFMLzt/LkJW2eCKEoWO06dqQ4e6A0JQskr4xu/eCXa/nzvANqa+M+vs56OFTyMhKrqkpltypAMlS31vLkd0v6CtpN4SpAqtfvN9o9p728a5tU6SoZ01tMOYj0dVNUoAvzkT/gdbY5xQF+rJZK/0YKHklfG136aJJ8nRgpSlfbWDdu4tNGRoofZ05pxzYoFcDNOpJ6KRFEXnELR0ZLlFKkiXFx/8aJIvc65DlaHxPR3L11sfD0T1m/eb/3ZTMmqAL+o1ZTNWB2/s6Y14z9vvjzRV/DmN58RLAwJQV/02nRAqGsueOXIqp91HaMC/eNtB6PbXWSxw2MLCgrywU7VD7c8htuuXRYlIa9e0o2FM4U+fuSaOTtcbDo+LA+i8n0/Um7Z9WVmu/zcojW2Jrt7qlYglAROCkXRMvXUNnCQXVuMiqJIksntaoiqDsoaRcu+suxz+V4wbh96Lr5v/QeDz0D3XaNGsYFJBSbL/+gWYP+TQfPz/U+mk+UnA1hxcPO0+ITzy+bVKRFcH8UKrKe5tmB//vJL8baB/cCjn49vj6uiKBmoXovWU6p0pLWeKuoTN27txcO7B7ht1HL2IzKZUT3fpCANnXMd9s15a1ItAnC/dzE2eFcCiCe4lOQ9/Nye6O9y+B3oxJnW/Xz/yX0YDQlTwnoaEsVKMxBk/deGLaynizXx/Af6R6Pv8uVHYsJwaHAsIn4MszWKYh9RJtqbs1EfR9qE/m/fd4EycOXceXJ1yHYSTwlSpXY/k+o81Mx/Dvp76KynFSmKQuiTrUqqq9USVZzhvIdb7noadzz6cuKxvg8cIWqVreWVEebB0SLK5LdmwSlZw4T+wvnTcGnmhej229wtWLx3I4qlYL9ddsYs3HvTZVi3ehG3UFEpDvSPcn1HuzW/o42iSK2nTdmM9vhl7zVS8JB1M+hs4d971rTmgACJk2QyTokLAyWvjFJI8tyMuUbyAfKb0mO4xJQzA/mYVT6CdasXRUnI9950GWZNF6434ThR8sqR1dX3gWskIUxFzwe72RymxtLWJRStOTdaoDAhVSsQrkZRWByIahTHcepuaz21DRxkr2cKs9HdL1MUa5GMT+dgXOqppaJIbaX3fhjo3RLf7t0SzLc3fCR+vQZRbGBSoRay/GQAO3mbpvETm2LKyRlXo2ipKNILx9Gd6v3JYFOjyNm9qqlRlFlPX4NEsZLU00hRlE/C7n5qPwo+PzEu+PFjf/zsAe1HMk2Glsxoxe0fuBgbTv8cPl28EZvLZ6PXn4lncC6+PP0PcXPxk1FS38BoESWvjH99LK5posf2D546iJJX1k6cWRImtZ4WfTd6D0e0Zmlw9inx6vwJSfDNiLg+IxyHJa+Mf//VXqiw/8SINQnRKYp95LO1N2WjPo5XLY2j0i85bQZUOHeu3HrHJvFLNKoowBOkSpvLK1Xn8PZzp7yH284RRZ2iSPpAPr2vX51WqlEUbVVSXa3WcUFR3Ht8WPu70xpP2xovRjbKPnBSUM6HC542mOq3zp+NT5z4PD6S3RRtm+P044odn8VXskE93DSizjbXgCiy44Ydrze/+UzlY23CbPKk7rM563IklKput127DBcuDMankUIJvu9jpMjvr2dDi3rJ56eJ//b4vui4OTDAj7tFz48VxYxjXDShiwcdrTkwzhVZlA3ko9AuuV8cC7NN0WLUU3tPRJu39QwkQphoHWhz1tVem14+OmzdP9HUHoazrtJzzxeI4mRQFDNZ+VhqGzho2x5DpzjKiGItkvFr1UcRAF56QP6YHffFC3ENojjFMNVtmSqw77Xpz/SPS9uwfiLglWL1r7mDJ0JpeiECgvW0ghrFns3mx6e2njZST42wtZ7SAZatTCqI4sH+US68BuDDbGR9B8Xn6zBWDBp/9w6WcI+3BtcVPovL81/BNfm/xL3eGi7OfWCkiI1be7F5X1zX1IJ4gv3MwWByYpo4d7XlOFWS2U7fdt6cKERkznT58dGSy0R1Xx+6/NRo+wmJojhUFHuA8UPKxq29XNKniJ+9eNSahCRrFOPvRyecbc3xdhqQoWu9caB/VKk2Zt1Mos+fCEqQbO2qIjZ4V+J+72JpjeL93sX4defbue2DltZTqig+8cpxdVopRxT517NRSU21WqKiSC2kMvyEKPm2NV50PwwK9lOqvL/l3FMiRbrJDdJ3b1+6E+f0ScKEELewoTZeGpq0VFHjp6r9YxCJ9UWL1GNG2jCbpvDzMRJKVbd1qxdhWnielP2AhI7k+de/b9tBXPPVx/E739zCbd/TNxYdN6L1tOiV4YVqoJtxjIsm9Pea3pxFNrx+ROeignyw32jkAkmIXUYMs2m2VqQpGW/JmRVRm/6JQLzgRG34Xa05uXVVW6M4CVJPZYmngD5w8Lx3x4GDboU1ihQyRbMWyfjVtsfI2fUNjkhwo0ZxCuG1YssUQb+XKV0pbcP6iQCXIDWNr8GztY8yVKIo5kkowahFIIhVH8VaWU9fL6mnttZTGq/O6v7kk+l5Xa2JdEN6WyQosufrwFQO0aZV9uNm8gwDo0Xc/dR+zjba6sQT7IKfxfrN+40T59ZsBn+w5uz4ecjiY2vOwEWLYjt02fdx3aqFWCUoDdv/+mpsCCeTtM8iDZ5gSGSOpFSinu3pt7Zqio3KafN6UVFkoBNRXeuN23+6W9veIY0ioHvsAk2vxzIyuKX4iUh1LncsQGH+Jfh08UbcXPwk+kf5z8aF2eisp81ZrYYcTZQ1rXpMx/jM9iZjrZZIFKn6JQNNprSt8aK/t1inSH//ty+dg/PnBSSuJRcQqcwz/w5AEiYU/r/OfZQj3XShYu3KhVyN3zlzpuG2a5dh48cuxxsVKa0yYn3OnGlQ7cIxG+upEGajQxs5T4bzJQwLCqxX9rF1Xz+2HeTPG3Zt2rT9ENcCBgiIIrOe5lzHuGiybGE8Rk5rySLrOtHrAFCSD7Z40nHJ7yVfVCRRbpM14RsTFNlUITQGZN0M6FrUufOmS3uFTrbU05LDXwtOlLLyvqkscPC9dwCLLgU6Fgb/v/cO4LrvxJ85CrNJWaNIIVMUbYmqDtRyXxFRtHSTMMdTI/V0CuG1YssUofteItI2rJ8IiAlS2WqIYiU1iuSknqZv+gvArkaRXoCqWSV8PaaeSomizHqqVxSvX70o0VibEsU3n6v/rU2TIaYSyCYV4mA7MFrEwf5RLlCjlSiKJWRxoH/UOHFe0N2G3zw/PqfzaMLju4/hCw++FG07OlTAPVt6MHtaM35+65sjpYFOXKgiJ1cUhQ3CcWiaaA3lS+laF5D91UrCbqitsb05PVEE9O0ddBY+kSDpHvvTP3mztubRgxupzt4nn0f59zfhnlB1HhTqC6n1VKcoln0fJrfxvb/eA4wQ5ffHf8Q5akzH+LnzpsMNUy5VjefFMJuMobZrGvkdbWu86H4Qa2rp779oRltE+obygfXSVOM03zmO6c1yRbFULnM1fn/17qVYt3oRmrIZXLMytsR1tWaVx03JK+PWe5+Dyh07YgjVAnii2JwzEcX4/BkpeAlizSC2m6C3RStvqRxbT92MY1xgYa00gOD3ZjWk0esS8uEvegOGW+Zii38OPl28EZ/w/hAP7DgqJywU2SZrwkcVxeasOTXW1D+Rwvd9zp0yIAuW8krArgfj25u/yTvbxjn1tOSV8Uf3vsBtGypl1X1T3Syw4gPARx4APrU9+H/FB/jfxNZ6qlMcZYTMlqjqQOcU+Sqtpzqw15sifRSrkDBeQ7BJS1rxgfH5LLWE6XtRpG1YPxGgJ27zNH7lLTVRLPJ/l8tJy0ri/QlRPf89wM926B8/rjWKrxNFkf7mUutpOPEq21tP165aiIdfOAS8Gm+j1tPLzgiaSrPawLufChS9uZ0tOGP2NOw+MgQHcYKpiFOmt8D3faPVDgjqC+d1taI0GB8LzYQoFuFiflcr1q1ehC2k3kbEuosXAW5fdDvv5/Bsz4D0sYwgrZNMxrvb9IriyYT1lD+G53W1Gr/39YbvwvDn//E8fv7SMdx+wwpk3QwfZkMVRc56Gv/mPSfMg/L6zful+wGILXyq+20fe/sNK3Dv1h78w/07pQFBQKDG5NwMcm5gISyUyomJPL1NCT0Fq83ShUL/P/bePM6Oqk4bf6rqLn17786edAIJW1iCCQm7SgRFcEQcwqIoMj+d0UFxmwVwZlxedcZRx3kHFR2Z5ecysoXgiDiALAOKKISEEAIkkBBDZ+1O0nvfrZb3j7qn6ntOnao6de/tzk3Sz+eTT/rW3erWep7zPN/na8DCDfu/Cth+GwP0veQ6UV57GLjyh1i1vAePb+4Lte+VTNv7LvoaZnF9fHOfH1BSQS5tRNbdLZrpk4hVy3vw4KY9+N8twR6gVJmLUhR7D/pkYX5Xs1dvaDtu/WJrxzzXTRSC3c40vkaREMVC2cYYIXKUQG3t9+9bP/rw2aH20rha3ae3HcDlS6PrsMT2GFHgFMWSiULIvhBDsUwn/D5VMm2PuKV03Zs0+cGvX8c3H94CAJjZlsVfvfMkrDqjB19/aLP33ramFNKVdeYIiJGCefq1uHHTyXhokN8+N63ZiMc393nXBAASRTEbex1ihI8GBjWljdhrU1z/RIrhvMntH9Ea7TnA6OT+8E7uPPQVxcnReO5bvwv/88oBfIcMJQpwrzVR941I1CXMJoSQMaJa7Xiduo64MBtFRTGjaD1tnwfsf3WqRvGwQj3SkhoRcb+LQVWWP9SgJ26mjffK11KjCKi1yKBEccnV4TYHhslMPT1qahTjFEVZ6mk5/PVwZ77//oolKJJAm/Zm39pi2jaXMspqvNa/MYjV63bi+d5BjiQ2pXWc3uPv+96BcRwYK3mz/VH2vOF8GdesmM8ripp/bJaQwtVnzlezQpLjSazBFBFWa0NTGAcEoug4TlBRFI7hOCXItBy8Z+lcpWRRgFf9qPWUq1EkA2AauR+nKALJrGTVImXoeN+ZC/Dc374d37jydMzt8Edh7NCgtkYvyVNUFClRDFEUVYKCrjB+g7fZv5c/WXHUsAE/s2sCbnP1eZUB9su7h3Hvup2RdWCv7gvarN55yizJq110t/jXL0PX8KFzj/Eet2Xlylw0UXT3f0rXMKejias3HC2YXo1TWJjQPdZKznqaJcdf0bS8tGGAdwq8ute/b5wwS2jdQBBnj/x9RK0vQ0lQxKJAFcWDkqAqHxrKhBzaEcNGV1F014HZSFOGjj8571jvNcuP6fKcCyOkj2prNu3tR1GpVK0xBBC8FxppZUW6wIXZRKfGzu1oiu2fSLFvhCeqAaKo4myb5NTTu5/rhQUdtuPfs/Lwr6mqNZoclNtjJAyzqQeMEKJYjaI4Z6n8NSdfBnQtdP8284dFDorS0aZp2qdVlh22qEdaUiMi7ncZTclk+UONolCjSE/eWlJPATVFkl44cp2+zaFtjr+8Zbr/d1Lr6VSNYjyqSj11bziOngq1xQ2Ml3m76an+OZ8vWfjcfS8qN1EvlG184OwFaKkMxN44OM4RkBNmhg8Wh/JlrFregzMX+XbXHHwStPSYmVh1Ro83cL9h5XHec/PEvl5kdrSIcGsiEE6QoqynRdNG0RauG8JxGNdewgFw5fefxltPmO6RpLhMVjY4oWSKztS3ZOSKIlWUwpDESlYrmOr4qYtO8Jax8TEdxDNCE1AUK/sjpWuBnpMMKkFB1xhPRL+gEnSWMnRPrW3JGLjv4+fjvOPc+ruxkoW/++8XQz8CCIZC5csW/nHVEu9xLq1j+YJO7zg4INQ0UqJ56/uXxlqlaQ0n4E8UzOvKIWXowbYhS6/FU+lzQ8OE1lhvRRshl1TRLgqKIkcUK+u9oLuZm8QQEWePFCdqZCiawTCbMNBjRrQFi6CqohkxbKQ1irQVSUs25VmJ6XFAe7q2cooiTxQThcqI98JUVrnGmCqK2bQesJHPbvfHHMsWdCm1xmAQj/+RosnbZlWcbc7khtm4x6TG3RsLhChWNbHmKYp1tp7WA5QoUvFAuY8iURQXv5tfTm2wWTIGOAwCbVRHptcDuFVY9ieSZYcnll3nhteE4XCwZcoQ97tO/qN4id4y3Zmu53/iKpQd89zPXXrt5JNLStSybby/O7GiKMziKCmKJMwm0+rbHIqjwEM3u8vHKk2zU7n4WSjLBF6+33/c94p7M6hm20qJ4hGmKFomMEhaLTzyeWB0H7+9ZKmnlX37h4ESblrj98Sitrhrz56PaeRyqKf9m+G/Prkt1LYZhtXP7cT87mZs3juCXoEonjq3A5uJykDB6qX++tJTgX93l9EaxU++42RvcJIydJyxoMt77uNvO563ARlUUYy+0YURpExKR0vGwFjJClhPRwomP2jU9EASCBto3ff8Ltyzthe7BsaRN23Ocrlp9zD+5r9922NTWkc+IriDbcswxaQ5pEYxHxOeAiSzktULYtsPAF6Dd4D2BuQHTow4djanoYU0x1QJClqUGQSixmzEUeN/p9tyYNMu/7yI695QEl5QNG2Mk/383mU9+NoVS3Dm3z+K/pFigLy8vMe//p4yRz4JF6YompbtBUexvp60BnK4YAK6gc+n/gorxh/Gh5p+g2lmH3Y503CPtRJrrLfChs4RxawwUUFTQ5kiNlIoe997YoSaCMTbtNua4gerNPWUKp4y0FpeGhwkAyWKLE154bQWbD/AW+fKls3VKFLMbM9itN9EH/mu0YpKrmlAc9rww2wEm3KiUJmAopj1rkO3PvYavlPp1zq3owmfeceJ3sQbwCfLsokoaiMvmTZO/LsH3W1ihx/wYpnCnM4cjpsetCWOFMr+5IaKs80LhJmcsRc7JktIIQt3XxUc/95Y1cSaNyHuuOOwsN9ySBTFEIupah9FSmBH9/l/n3MDcNHnyevI+pfG3fFsAyOSKGqa9n4A1wJYqGkaGdGiDUC8D+JwwdJrXSIkI1WHiy1ThqXXut72MDtD93Hy5QxhnvneZ3zPvFGDCpYUtEYx08qrRmGKYBjRFS9CKkSTKZp6midhMuU2rj5Rtm1LI3w9QpJtK7WeHkGKItteb/zOX9a/Obi9ZNbTysxkf15eqPXQS3vR2ZLGSRxR9LddUpIIuAOXJT0uIdw/WsLP1vuDgGe3By+dubThEZnhgonuNLG4af6xaqT5GxYlb9Rm6U5C+MfWCdpOXGU84Q14RUQRpM7mDMZK+YCiOFY0YdF6pZBZbjrQumdtL0fWZWhtSiNfDj8f2eBE1zVkU3qgzi1MUYxDXHuHiYKMKFK1tJ1YT23b8UJgmGIWFWQTRz46cil0zV4I7OwLX0HiqGEEvz2Xxn3rd+GVkAkPGWRnH1UNWbuG6a3ZClHkj4FXKkSxqzmNWe3ygRtHFMnxumeo4Kk38ytEkZK+kQphGS45WG2txJauy9F7cDxwzLdz1lNao2hJFcXX+vx71omzogeDcfVwp8S02gD4JNlkiqI6UZzWlsNVx/fg0Vf2BV5Xthy/RlGokZzV1oTX+8fQN1yE4zjQNM1TFFszKei65hE2MaBGtcYQgKRGMe2tz7uWzPGI4h+fMS9QX0eTZWUTUWmDhg/J7ydRtboihvKEKMbUyKKjBxiu9PSdJEWRHZO0Jy9VFKuaWKPOKascThTDahQ1Q13hS4qwz63Gejrit/cJuMsyZNKoNAog3ILfCIiznj4N4FsANlf+Z//+EsAlE7tqkwjdcGvOKGa/qTFsmbX0d2QpUG/7O39ZzlcgOCunDI2WBlsSwmyiUk8tE3juh8A/HS9ve7J7Pf96JaJY+f5sG6+ctEuIYpzttN7b9khPPVXdXl7qabA9RlQIw1Ov7UfJ8W8SRqq2G9GcjiauZunhl/1BVe9AcHacNnQfHC+F25CFGxkfaELS5O79E+DRL3jPtWt5fDN9O25Lu43DKeIIUldL2l8vgtGiyQddKFinVayQb+qJPnfo4KRJ0vScKiWt2ZTUHvbFy06JTS+dLMjar+Q466m7/R0HGC2ZXrLo7gH3mrd3uCCPq0d8jeho0cL3hs91Pz/sRcRRw463jlxKuf9lFChBYftteqs7ED04VoJtOzAtGz/9/Q7PwmnaDlY/t1P6e+nEAFtX07Lx49/9wVv+5JZ+3LO2lztOGGFh/7dkUpyqy9Aakno6XChzaaVly8I9a3vx2bs2eMsOjpVCe3UC8Tbt42bEB2VwfRQThNnsJyrfO0+dhcWzeVJLz/N0Oo3V63ZKU5CLZctrbZGSKIpsHdmEA6tRZCFBaV1uPVWtMQQgST31z6802SZlCdErcjWKwf2vaZr3u0ohrXRU6oIZOJeASh/ASW6PwY5JmfW06ok1eg+LCqwJS0VNNwd72NQLshZamqG+vWmYDSP1gIQoEkXxcLeeOo6zA8AOAOdOzuocQgwLsv97bgXmLjs068JQD0XPSAGLLgD+96vu49OvAZ75V/fvQoxSMplpsCoWV8762cYnRlGiKNtuIsaE9DwrgaKYFSxEshrWuB6K9d62R3qNour2YtvBDlpPxV6JFANjJS7wxVXuIqIiY1C0bKx7bb/y6xdOb/HsqEP5MtASsq7CjYzWLXmD5BBS7TiVxuH2b/BE7mIcM63FDcYh1isZWPLp4HjZUwKAiqJI5xoVbqYqVsjzj5+OlK5LB1vi4KQprWNI+EjaR1HTNHTk0oGwjuvOOQb/3/kLY9dlMjCtNWh3kllPAeDgaBE3PbiF2zZjRUue/gjEppVatoNv9a3AovSZuNRYG3wBcdQUypan3nbmMti4U6GXbAWtWQOjxeDkJrU8MhI2o0Kc3ZYaRXzh5y9x6z9SMEN/b1Pa4FJiZerOrsE8blqzEctI+uhIwSXg7Pe1ZA00Seo++RpF//mBMX7A+59P/QEbd/H317vW9mJwvBxYZwbRpr17MI/ulgw27Xbve1EJsQylKmsU+wlhP2vhNNz6vmU47YsPezZSShRfPxh+r3xq636PDIvXlFnt/v1o30gBXS0ZX1Gs7PtAH8UKoo7jAGGR9FFkoORZtEIDwfYYMqQNHaZtcevIrKZ3rX0DLyQ4L7i64ygHGDsPH/uK+3iSwmzYMdn3FVrvnsHXVy3BlcslPSBVQO9hUfbSMBI5UfWJgNx6qpp4CoQriqLDjBLKwyD5VMnbpmnaFQC+DmAm3KwBDYDjOE68F+JwwZAwO1psgEaYKiqKCpmgBIuSmjiiOFlpsKqEWGyPUQhRFJP0j/Ter0AUmaKZFQ775unuxYTWOcYpivXetkd66qnq9mIzjcx6atveLGwZ4UQmmzZQKlOi2ASgugTM0+a2Y9Ou4fgXEhwzzb9xDOXLQFvIugo3LVrr51mYQkg12zTXpn+Nb/ztN0Lr2kSwzzVtByNF01O4xkqCoqhgh1JplzGtNRsYMM/tzElJrUz1ac7yyzoFotiSMaQD9UOFbMpAZ3Oa25c5znrqH5f//fzu2PRHaqcTycfWvhEMCrWONnTcWP4UrrB/g5tmrsUMq9+9T5xxnTs4rVxb6KC2I5dW2pcMlCSymleAD1FpyaZgWjanMl5+22+xJ+Q7wuL5O3Jp9I8UMThejlR3nu/1B/QjhbK3TmxdZMcWrRNM6Rp0zQ0gOiio7SJJjFtn7zOFtip7hwo452uPAVAjiipEh4EjioSwt2QMNKUNLJ7T5l3HTMfwUqbEdhkUT7223yOXaeEcm0ks1n3DRSye7Su4TFFk56WYesqO4+v//2fx262udf+0ue340HnHBie6AjWK/jUznfJfJ5JRgLfuytwK7u/SkC/7qqdsMkIVXJIxc4A9833gVxUXWMtM4O1f9M/DSVYUAXef0EnUcSeDi0+cUb37goobUYE1YSRyQomiRFFMJSGKhADSGkVxPJg+vIii6t3yGwDe4zhOh+M47Y7jtB1RJBEABgWi2Ag7L05FefBmNRtqgQxam6f5hbRxRHGy0mBVbYUloUaRs54SopekfyRDksr+5G4AACAASURBVPYYGUFR1HWgfS6/LK5Gsd7b9khXFFW3l5h6SmYloxTF7uYM9zwNs4nCsgX+fu5uTuMbV56OTEyIhAzHEuvpUL6sbD2VtkiIIdWz0R9q3ZOBtsgYJMrJaNFKrCjGWcjY97EB8703nIenP3eRNOESCLGeCsmSHUKdYntETd+hgmg/pWoWVRTvXPtG5OfI4urptjxuprxOzoKB1dZK3JD5h9BG2aLNWWVfykAte5Sg5NI6brzjefyaKPFhJJFB9nvZ9hrKl5XtsSMFE2MkgbMlmwocW9mUzql0mqZ59sSByPYS8escBkr2ihEBTwxJFEVqveXay1QIeys5j+gkm+2Ef+7BMf9zgmE2RFEcLrjtdQqCosjaY0hIXMrQMbvdJwk/+vBZ0muCLPWUgbeeVq8o0vcnsZqKEJOMYaSAJVf5j0+6hD8PvfYYk0cUbdvh0q0LyISGsSmBKopRRDFUUZygIBtAThRVg2wAnsTSnATRYXaYKYqqRHGf4zgx3cUPc4iKYqkBFMU4FaU06tbbrb4+3M8N8Ipitt2f3Ygjiiqe+XpAxVYICIpiG0+EaHsM1f6RFHHtMRyHWE8lg612gdjFWU/rvW2P9NRT1e0lpp6SWcnuNnmNzyWnzoam8cmgKRIac/wMeVrh6fM68IMPLvcev23xLFy9Yj72il7IGDRnDK9+B4gjijyBZYEdaYO0SIgh1TvtabhpzUZ84qfrpQMyEXyLDH9APFoQaxTjBy9xdViAb3VVQVYYzGsa37YACPYYbFdIj5xszBCIIqcokvXdNxztfIiLq0+UHimAKp7tubTSvpSB1ob1kd5yL/QOJR5wy9a300uJLStZnQGXKI6TMJqWjBFQFKntlCFbOdZUWldErXMYsrQFhxkzIYxkRJH+PqrsNqVcwv777Qe9ZZZieww6CSPWKM6iiuJIEUXTT0hl29YjiiGTWCNEgQtNgY1SFGNrFPn2GDL4RNF9f9JaXbpdAr0UAZ4Eismqh0BRHCmYgRrFLTURRSHMJgyhNYoTqCjKahSTjKPC1u0wr1FUJYrPaZp2t6Zp79c07Qr2b0LXbDJhlXk/MdAYLD9ORWGICz+himJTAqK49NrwpvL1TINVtRUGFEVyAlNFUXW7UZgxN/rSGLyaNRlRFL8zznpa7217pKeeqm4vMfWUKMULZ/L7ZFpLxgsxOTgyjk74N7+FT34GVxlPQIeNty2eIf3a95+9ABYJzWEDizkJI8O7mjPBtEZlRZElX2Z8K2kIqaaNwwFJo+rQ9fO/kw6I3RrFZNZT2pesg1gqzzzWD9nqalEnijlhMNeSSQUstWIqKLVyNgrE5NOwGsU4xMXVxx2bUe8XFUW2L9+1xCeLJ85sje2DSbF5j3/O/Xabek0vg2x92fYaKZqY3aF2DRwpmLw9VqIoyohJU0VRVBTnASRrKUADVQoqimKV7TGou2DdjoEAYZe1x5BhSY8/QRpIPSWKYt9wgbNdMkUxrI8iA7OqiuouB3HSNKxGUTJJViDW07DtJ9ZRqk5GMND024CiCPD3ckeYHGDEcRIVxaE832O44GRrI4qGqqIYEWYzUZDWKCaYWMyEBE7Fpp42NlSJYjuAcQAXA7is8u/dke84nDC8m5eJgcYginEqCsX6CFWuWkWReebP+bi/TNPrnwaraitkip6RdX3jdPaGKoIq202sM4wLs6EJsWKYDRBMPo2znrJte/ltbiPW9h6+IWtd+igeQURRsr2c+Wfj2dO/gqsOfAznff0JrPr+09g9XCEyjpsM+eH/9NtpvD7A35RPntOOq1fMh2mW8FXzWzhe9yeLcgc2eSmhI+P+QICmKpqWzc/gVwYhSS15nc1pdJDWFq6iGFajKBDFispDyVwYqaaNwxlUbHBU4aOqkpt6msx6CvhWyHctmeMtK5GBYXcCRVEczMsaz3cKn9eQiqJIFGnqaQKiGBdXnyg9UgBNvWVkLGXoePvJfrT7DW87LlEEFFXXktg3GWTrS4n1ZafPDTwvw0ihjHFqPc2kuH0ARCuKSZCkpYCha15LBhVFscgRHfUaRYonX+0PLKPWUyfiPD9hpn9vDEs9BVxlnNlOAaA16+4zw1MU5aSYpaRG9pQUSVSKKor+OsWF2YjOBIYMsZ6alo10zHYWsXfYH6twNYoM9F4ulhVViGPfaBmrvv80zvvaY1j1/adDU48pWFpy0vcN5ctcjWIBafzihd3K7w9AVVEMs55mGth6amTkY7FAjaLQR7HBoTq1qgP4tOM4gwCgaVoX3DYZRwZE2ynQGCw/rg8iRVT4CUdy2vyDtjjszlDpERc6IwXMXkIWaNUnnYYmm35A3sOSgdkKvTCZys2IUxQJUYzcbhrwnm8Dv7sN6CcEOi7MhtuGkvLcpIoi4G7bZR+sT3KstD3GEUQUAW57eQECz+4F4O7H3UMFvJQexdzKprh5zQbMwgBQ2QzbDvD7mNnextf+Fy411sJx+NRtlhK6t+9BAG4CcndzxiNLRdOW1rREJfQ1pXWcMqcdJdP20gzfODiOJ7f4feySWE/ZunD9Ahmp3ngXXrj/O5hu92O30DicQcUG1xmhKDrQYTsadM1JPLlBFYYte91tkU3pgQF6FJqEWX/avoAhqCg2HlEUaxSrURRV4uoTpUcK4BRFMrFBSe5r+5LdN+n5M6u9Cf2j6mQxbH1pTepbTpyBty+eiUc3B/tEXnLqbDy+pQ8l08Zo0fTUKoCF2fD3RSlRDCEJJ81qw5Z9QdWlmpYC2ZSBsmWqpZ5a6tbTbEr3wngoDkh6KlIV0YaOq1b0YPVz7phjRmvWS02l6yjWDjZnUmjLpjBSNLFvpMBtb689RoXI2Q64nqEMzHoq2xceAtZT//g0dA2a5l7b48JswhRFaj298Y7nseNAsoE+DdYalLQYiVIUHduCBuDV/nGsK7t9GVmPRlkKMENUb8eo9wHuee84vPXUtB2s2zGg9P4Aam6P0cBhNprmBtWUhPGieEweoTWKpzOSCACO4wyAjZyOBIhBNkBjEEU24Puj/xv/2qjwE8562kFIjAOs/bf4Ho20lYRjRc8ChYElm8p6Gv7q8+Hvo7ZCMUwmrI8i225LPxD8vHQzcMaHgraGOKJYEsi2iKQ1ivXGkW49FRAWIOAQ45sOB2nN388lYV6srxKkkX7hpwCCrZnY43MHf+kto7bIkqgoVgZm1F4p9up74QsXY2Zbk0cSAXeW/Cu/9EvAB6OIIqmhKFu2N9iiiqS7Ei6p/vLM/4vzi9/BVaUvYbW1kiOJgJoNrourUfTPfdZg3FMVE9qhZhOiyGx13Qlsp0Bw1l9MPAUEEo2YQeYhQqSiqKCAHjutWakPJD0228l2+MdVS2LfL6aeytY9qSWNcpQrlsUTqFzaiOx7aVo2dh70Jz9uvGM9Tid2yJYM/362DYbzZTxOyOS//eZ17BT6nbZlg/shjExcvnQuvnaFP8HamUtX3auTkdGkqadxfRQ1TQsEPwHA7I7gNYFTFLUUpxx+8JxjvL9p71iq3jEwVbFPUBTbvDAbUkMoURV9RTHiHI5oj6FpGqcIiqD23jC1mFlPhwvlqkNsGKQTK1yNYpAoAghcx4HoUoKowJ24EgTRepp3+GuVagmDh4YOs5FZTxNmPYhEViYaUKJYbnyiqKwoaprWVSGI0DStO8F7Gx9SRbFBdp6RAk77Y+CXn41+XVT4SZj1FAAevMn/O6xHo9hz0Cwk820D0cmmRYkFds5S4Kw/4+LZuYb3QHjqKeCu+6zTgp/LLj7iBSqJ9VRMPQWqUxTriSM9zEZAWIAATeHU4SBNmsuLse6D42UUTQvGyG5Eodv0B5CUNJVCFEUgGHPPcM/a3tjBRZSiePe6Pbhn/S7sGcxjOhmgi2SI4ZoV87Fux0Dod6nY4HjrqT8bPl4o4irjCRiobIORPe5EE+19GoFZkvox0SYaB1F9bJYMfMVtc1hYTxMqih9/2/HKBIQdm89uP4h717mq0EWLZ8W+X6xR9NadqKFURbvktNnYvGcYf1BUXK45az6e/cPByPPjT84/Fjdfslj6HFNNHnnFj6XfvHeES2j85EUn4M8vOM573JpNYf9oCa/vH+Mmb7bvH8P2/fwYoDWBougA+ONl8/C5+14EAJx3/LTQlhhx8IhiOd56WlIIY6HIZQyMFPl74aoz5uFFob2H6ZDzzDA4JYweu7TGT0biGXYN5vF3P9/kPfbbY2jk9Q5EgwBb12hFUUw95a8pGUNH0bRRNmVhNuqK4ngpen8YuoamtI4xSf9Qhp0DknMjqkbRCSeKgHt/kR1ncYE7Ye8D3PM+JYTZJHl/AJz1tMHaY8juu0n6KAISoiiIBpYJvPqw//iFu4HpJynfNw8FVBXFbwF4WtO0r2ia9mUAT8NtmXF4wzLdgc2ztwefiyKK7H1xSly9EBe0Ehd+wmoRU03uRTOOxIjhOGNCyIBKz0ERSVtWnPKeQDy7p/LKFMWyxEJHyV2uEpbBLj7ivkpkPZUoiq1C+t9Dt0zsMSHiSK9RFBAWIGBziqKNNPwbkaz/V/9IEaPZWYHlFLud6d7ftBawaMoVxSioJOSt3zGAK38QtGKXHAM3/2wT1u0YwO6hAjbu9Adz7SEDp6hkSlUbXFuTv91WP+fWuKx+Zjuu2/klfDN9OwytMuAyC2opzBVQRZGhuyUZiRMHcy2yGkVBbW3IMJsI66mMoFBUY2cE+AE+bVMRBkoUqX23qznj1aNRFW7liTPw2F+uVEpGNXQ3tZepnWw/GpqGj7x5ob/OreGTXyptCmYKhJzVusUN+t3XBvdDWK89y3a45E4jqrwjBizZt96KIsAH2jBcc+aCwD6j186iqXHOAnoc5QlRpOogI/Hb+v1x1evk7+bKb6R1jWLyadG0vOutTN31EFAU+X3OagplYTYq7THYdrVjavNmtWXRETMpJd2nYamnjgO9osFbIUP3sFKCuMCdrX0jobWGgTAbBH9TkiRfvo9iFTWKk60oJrGeAsFAGzreZs66R4iLbqwv0X3zUEDp6uU4zo8BrAKwD0A/gCscx6miWV0DgVohRcUM4IlB2PtEC+VE7Wiqdh37VqDTt3og3QyM7AM23BFOSpiiyAiOitpFw3FGhfoOGSmLQ9KWFZt+xj92HFKjyBTFkNRTBqqk5rrZB7nbKan1VGzNQWGZwAOf4Zft2zS5J/9RpiiGpTc6nKJoI0UURdF6CrgD5Oenu8EvjnCfZI/vMC/wllFrpKso+p+vMjBTScgzbQfPvTEEy+Fn5KP6QO4elLd3ibLBqtjgTMvGLWte9B7nyzbW7RjAs/d/DyvGnwpsMwDxKcwVyIhiUkUxEGYjGfiKNYmNqChOb+N/N+2j6DhOwGLb3pTC8gT7UQZKuvoldWkimIqka75VEAB0XQvUWAKuTVt2/J0yJzjR1pIxoGmap3a+5QQ3adhyHK7HKA1EEaEyCTOzjT/mktiQZQEqYWTCtB1YJKBJDHZJgmoURV0Lpo7KIAbaZAy3RljcZy05f7vlbQ39pK1JmKJIf3MciX9hp1vZRNdZbN0zQsNvEtUo8udWWkgtpSgohAF5qmfMLp3bmUucgA0gXFEk47swRTGslCBuPQbzZmi7pOHxPOZovljwudSdXiJ43PdKQa2njdYeo9YwGyDaeqraM7zBoHyVdBznZQAvT+C6TC6idhgAHNia/H1sR9cjnISCKoqzTnWtFYM73MflcWDns+4/0TLK4PX/q4SwqBBFGo4TsJ5WoSh2zHNJtSpG9/GPy+N+Mi0Ls9E0VzUzC/I+iJTsN3cDB7e5f1vlIFEsF1xyHAjaqdgBohTFF+4ENj8g/x0TdUyIOMpqFMMslZHW04p9KmPo3mzyz9bvwkPbT4NlnYlLjbXcZ7GU0HvNt3jLugSiyCuK8baROZ057I5pIu6tLwwYMYoow8t7hkOfC7PBquC+9bvw8Mv7AsuvMZ4AEKzr9LD+J7HHfGdzGpmUzm3DJImnQLBGUaooitbTBgyzmdaS5YJFmKLIlBixNcJwwcSM1iyuWDavKpIIVK8otufSgZCRGW1ZLs0RcNvPAMHjb8eBMVzwzSe414ohRLMIIdy0yz+2oxRFlUmYWe2ioqhOFGWqfZi907JtLrmz2n3kfoe6osjOpbjWGAwiUWQ1vtw+s0zg374EVHjebelb8ejey/A4zkIqleK2YZ4cpwaxkcaR+N9uPQCAr2sUFUVKFBPVKKZEohheo1j0tp8eaLMjvl8DIhN+rz5zPuAg0vrvwJ0I4r5L0/xPJ+TQtExvwB6mKIaVEsSVIABureHqdTthaBrufq4XewbzmNeRwV8Nfw3LdX88fJK+E9/Ub8eF+vO4sfwpWDASJfkqh9nQ54yM3+pqsq2nSRVFUfGkCfgqPcMneqxYBar3QxzuiNthYSmiqs3h6wmqKB7YCrz+hPx1YTMSLMymKQFRpOE4Aeup2kCXQ5JWHwDQPJ1/XBR6KDIw1UyqKFKiOM3/2yoFieKme6NVYq7OUyCKh+KYECFNPT1yFcUwSyW1nhqC9ZQpctRy9OPf70DfmIUby5/CX5c/irX2iTiYmgln/tm42fwYPlH+NDd72y3UKPJ9y+Ivp0laZ4j9ymSKKMOIrB9XHRA2wJujHYh+Y1QKcwWapgVUxa6QWsswiE3RpTWKh4Gi6DgOt+7feGgz7lnbi3vX7aw6hCIOVAVMQhTF7QkEayyB8GAicZ8BQQvkTHJcvLTHt1jLvodBRb0RFcXWKAujAFmibhghs2y+N2FdFMUEqacqNnggeL4Ewm2Yg2rvRm/Rm/Tt+Mv8t3Fb+lZMa9I5J0WhJFcU40j8wbFi5T1EUQwQRf8aF9keIyL1FKDtLWQ1ij5RDAMjirYDvPMUedkCs4NHWf8B9xiR9sdkv6GiKJqWjc/eud5/n0TOPG1uO+56tjfQ+sK0bNiOo3R/+vpDm3HTmo1eecOxu+7H2YXfSt02lxprcYXxm+TWd0rGokpzqNpIx6wTaT3VNF7xBKqoURTWj667as/wBsPRSxTjdliYanYodjRdl74YUffBm4N1k571NAFRZOE4jiMPs0mKpdcCPWeqv/74C/nHpRDrJ1PNzKgaRY0vKLbN4AXqwGvy9WDkuxRCVIHGOPmPshpFZmmjA8emlM6lf2pwkNL8/VyOUOQsGFhtrcRVpS/hjNF/weo3/Qd+oV8YsPhQdSos9TQKcQMHClP47ijradQAuhaEDfD2ONOkyz1EpTATBIhi4tTT+PYYosr41V++XF3/rwkCUw1pu4DX+kZx05qN+McHN0e+V6UPZhiqVRRl4ToypW9ai/yYbJKovgGiGJKkGnWcx03CpHQtUJ9aq/U0rNeeqyjSGsXaiWJBwXrKrPDqRDGmD2mIg4oRhVXppzziBAAFkxJFf3kciWdJq3Q7iTZImpIaVpMNQFKjyO83tr6yPopsG4fVnrrv99fxn69+kzchktK1gK0/zPp/1sJu7zPkvRQr318Zp9y3fhcee8Xv8yvel5pSOjbtHsb6Nwa8thc3rdmIG/5rHT5xx3rcct+LShMNYruOMOcIe3zTzLXJre/KfRTJZD4dv02koggEiWHN1lOy7qo9wxsME04UNU0zNE17XtO0ByqPuzVNe0TTtNcq/3eR135O07StmqZt0TTtnWT5ck3TXqw8920tzBOQBHE7TDbwVnnfROxoejKF1U4ylEZ5RezuD/qSvaqiSMNxCkNBe0A1RFE3gIUX8Mtk6aEMC87lH4eljnpEMUJRzLbxNwurnKzFx/qfRFtPG+HkD1hPteTJtIcZxkoWN8BtyaZwYNwfqOiwkZEoiiq4Z22vdLBFVZKiafHhEQqDM9nAoUXS0gEIKopc8qCAS5aokc+kCBvg3W2tBBCs6/QQlcJMICafdtVoPRXbY5iWjc/c/QK3bPPeEdy0ZmNoTc5kI6p+azBGKU4UIiGAI4oxNYqO4/hEUbKPRAJnSEgZg0xRDFpP/eOCKT9NaV06EcAQNwkzu6MpYCeMJBwC5H0U5eekaTt1UxQZaTEr6lAUGPlRqZcGgopioMY3xC3DNuO7zUe5pNJ8iKIYR+KvXO7eIykJExW/YVXraSD1VAyzCa9R9BTFiMRYSowdTfPW+cRZbbj3hvNw9Yr5HHFiNt57bzgPT3/uItx7w3lYNt8nD0Oyc9xTFN31ufu5Xj9dGkHraSGEBP7q5X14+KVg6YAq4pwjM6z+5JMgyn0UyXN0zDWRiiIQHDfVM8wmzlmneN+cbEyGovhpAK+Qx7cAeMxxnBMAPFZ5DE3TTgHwPgCnArgEwPc0zZsa+j6AjwI4ofLvkprXKm6HhQ2yD8WOptbTpP35tvyP/7enKIZ8RioHXH4bcNWP/AuVaDsFqiOKALBnA//43bcCb/4L/vsZLCHplSNqMqIYUaMoEkVbUqMYhaGd0WE2jXDyixMbqaaIArIjA5uECPeRgsnNtBpwkOJq/NQHhbsH89yAgKFTtJ4qpOSJEAcOX3z3qdLXJVEUrzyjuuj9OIQN8NZYb8WD1pnyQywuhZlgtlAzVquiKFrnaukfNllQCWEJQ6IQCQHtTSlvcmN/jKI4WjQ94iNVFAWi2NWciazxEnvsiZMlsyRBRzPbgkSPgk7CyBRO2WfKQlFas26vxT97y0JuuZwoys95u56pp+Q7ZEmdFCUFokMhbvdAjW+MW2amvZ+7TtLUU1qjGEfi33/WAgBCmI3QR5FaTyMtw3Gpp0xRlBJFd/2jajwpMS6TFkmqKi7A10kPy4iioCjuGcxz4TFhNYr1Rr2cIxy4MBuFPop6mh/zTbiiWKv1NCLMZum17v1RhgT3zcnGhB5tmqb1APgjAP9OFl8O4EeVv38E4L1k+V2O4xQdx9kOYCuAszRNmwOg3XGc3zmO4wD4MXlP9YjaYUA4kTgUO5qG2Sw4u/rPibOets4ItqQY6wu+rpowG8cBdq3nl1kF4Njz/cdzl4Z/R5j1k80WlqOIYnswaSsJUTQyblAQw8v389bVRjj5A0TxyK1PNC0b96ztxV/fyytFpm1zNYoabC7MJsp6KmJuZ046K9/elPJmyqtpjyFD2CAqSY1id2vCm5kiwmtBda6uc5czDVubToN12Xf5iaYYiIP3pDWKgdRTYaCr0j/sUEMlhCUMiUIkBGia5hGqOEWR66GoQBSnxRD+AMGPCLMJ+w4Z2CTMx992XOA5sTUGILeTfuGyU3HvDefhktNmx742MvWUEJ2UpPm8KihpKcrq2QiSKoqxfUhj3DJD2Vkc6Q9LPaUk/phuXhFqzhjetZNrj2FVGWYTqFGUW0/LUutpfI0i3bZlUn6gOlEI8ERRrihWPqtSozinM8cpirZT/6G77LwOc454j6uZBFduj1E5low0MH7QX/7YVwKtx9iYYNX3nw7UaCZfP9F6WscwG91wAycvvw1oJ+fWBTcnum9ONiZ6WuJfANwEgJ6RsxzH2QMAlf9nVpbPA0Dv2jsry+ZV/haX1wa2w878iL+sexEwe4n7t1mQz3aw951yub+sZVZQiasnqKJ4zHnRBDcKzHrKCKMIWdsLWeuQJO0xWM/J2y8A8gf550rj7j+GnO/bD9QccooeWf9IRZG0BaE3C7OI6LwyAQOvA3mSGPbAp/m2F/Tkn38O0N7j/j+Rx4QITePJ4hFan8jquW5aszHQEsJx+NoNN/WUWE8ddUXx6jPnS2/8LVlfhRHbYyQZKFCwQdR15/ptb2a1ZwMpp2GKYkrXpGmf9QBbt6uWB2eOaV3n+cXv4O2Df4M1zspEx/vsmq2n0YQjjoTVYt2sF+Lqt8TUVoZq+ydSMPIVV6NIa5dUFMWwIBsG0X4qWko7cunAxEtU4qmI42YEyxrkRDF4Ti2a7lrHxGNLqigq91GsJfVUXgMog0oYC4WowAds8CFuGUYUXpp1OVeLyFtP+XVgJP7fr1/BLaf7PirMhtbwJks95fc72zbSMBulGkVCFG3HJ+dJiCJZf5UaxWtWzI+0ntaCtKHhG1eejlsuXRx4Lsw5omnApvYLqpsEV26PUXnOKrntxhj2b+GCBumYgIXwsBrNqsoLxOTTpJPuUWE2gEuUl30QeAtx081d1rAkEZhAoqhp2rsB9DmOs071LZJlTsRy2Xd+VNO05zRNe66/X0JwRBgpYPab/Mfv+Aowg5ws5bHge9j7Zp3mP150QVCJqyeoupbKuaTksm/7y2SJlzIwgpXKyH3eqkRRVVGkPSf3vBB8vjjitr1gaKZEUVQUQ6yn6ZAaRccJt57Kgm9ao5uuByAmzLKT/yMPA3/xkvv/RB4TMnBE8chUFKOshA6Cqae0j2JUewkKNgCXWU9zaX/2O6AoGtXv65Sh412nzfEerzimG5Ywaxy2/p3N6UhLXq1IGTpe3x9yLRSQRKEzLRsv7+Lbevzv5r5Es8BNwuBMVBTjSFgt1s16Ia5+6+ZLFlfdBzMOjOAN5cvcpIcIao+TEVeRxMUp3OJ+Egm+pmkBYpcksOm4mRKiKLGeylTCYytEUSSzSaynbj1hnWoUq1EU6xVmE+KWYa2Dehe8h1MUqfU0TEU9dloz9z1D+bKn/tD3BPsoVpt6Km+PUbJsOIJUpkK0U4KCyghtkolCOtkyNB5Vo+huz1XLe3DRSb4NNKyPYjXIpgxcvWI+rloxP+Aeoc6RbU2nwWqbh7X2ifjr8kfxw3lfqm58w4XZlFwh4T8uDoYwMrUxzP1VGYPVvbyg7mE2IQ6+Nv9+j5E98tc0CNSn2JPjfADv0TTtXQCaALRrmvZfAPZpmjbHcZw9FVsp8zbuBEDvmD0AdleW90iWB+A4zu0AbgeAFStWqI02CqTOqamdL0QtjYXvZEpMiuE9zOoCWq9npF1Ssvx64NEvukpXR4/fVzEKTUSJa+rgSRpQ6VXo8LVttdQoxvWq3PkskHuH/5gjisJ3hLbHIKmndN3Leb9ZbbaNn8WS2VRnngKMWq8WvgAAIABJREFU9sGbg+g+HoANHHw9fP0breeNZgBMQTtCFcU4K2GaDKp0zUFa828y82d0YG6hCXM7c3DA97fKpXWcOrcDV585H6vO6IGha4HBVnPGgK5rnvWoZNoo0vYYinVBYehq8Y/RXYN5ZUVRpvDUG6r2SFWFjs0Cizf4L9z/Ep7edgDfvXaZUsNw0TonKlNx/cNqsW7WC6uW9+DxzX3Swc4lp872wjGq6YMZB0q+9o+WMC+EONNQHVkfylqtp7KQmlntTdg54B9PSYjinPYmNKV1rvWATFEUv7etKYVpLRmYlo1Hhf6hv9iwG1cKQSVhiqIdCLOpoUaRXFfikivZ9Ui9j2Iq8rHnltl4F/LP/BAH97yO3c403GOtxBrrrfj7lhw0TUNK12DaDugcj2wSw7RsfPLODRgnymPRtHHTmo14fHMfls73x1ui4qfeR1HY1gGiyPdqZI8dx1EiitR6Ol6kjhK1bW5aNtZu991Vt//6dTRnUli1vMffZuw3VMJsDF3DVy8/GajoA46mI2NoKElUUVVMb81g/2gJ4yUTjuN47pFP3LGeC8BhzpGmxdfjpktOwlVf+hUA4HKnyskPaj1d90NeLWRBjK89rBY4uP4nuLu0KPIl96ztTXbtDNQoJry/BsJsQjJB2ggpH5ET3UbBhCmKjuN8znGcHsdxjoUbUvO44zgfBHA/gOsrL7sewM8rf98P4H2apmU1TVsIN7Tm2Yo9dUTTtHMqaacfIu+pHZTkNXXwJKQUMYtO7aCFCSaKnKJIbna01+DxhHBRzCGKKQ1hkRFgxw6GyEgVRUWiGNdfcPeGCOtpRI1iVlKjCPDrLqaUximK+QFwQvVZf8rXhsrQaD1v6OzeEaooxhEWjWwDDTZ62vz9fvO7luDpz12Euz56DtqEAeLlS+fh7o+dyyXWiUSRqR5s4BZoj6FYFxQGarvcPZgP2ItKIdbZvcOFCW/3oNKjDlBX6Oo1CxysUeS3UVSIRj2sm/VAWIR+vVTDKKj2UqR1VLKJiZZsilOJ4izEIsGXWafFOkUZ0QuDrmtYNJ1XFWWKoph6umh6CyzbwY13PI9/EFqT3HzfiwErW6SiSIliTTWKlCiGq76Ok9wGGRtmA3humcH3/wLnF7+Dq0pfwmprJWzoXl2bzH2RlpDjuPP+pT3+WEq8nlVNFMXUU6HGkIGScFXr6UjRPy9UtjmbILvtiW3est1DhaBN0rOe+utUNv3fP6erFUt6EgYbwt2/7Nqy/Bi34YDt+L89ZeiY1ylPFe3IpUO3XSLQSXtKEile+YVcpBAxtLP+5QWB1NMpRXEiFcUw/COAezRN+wiANwBcBQCO47ykado9AF6GK4t8wnGYJIQbAPwQQA7Ag5V/9QGnKHbwswFRrSgoiZhURZEctC3T3f5/Y/vdWb+tj/jPzTgZOO9G9/cx22dWUBRlKI/zJ4aMKJbGXTVt/Y+B/a+5FgEjDUw7ATjjQ65dRTfi+wsGrKckYStSUaR9FMlJaRb8deeIYjtvd5ApiuJFafyAW8g/HEEGG63nzVFQozinM4fdQ+ETFc3ZDFC5LxiwccXSmcDvK08aae9G/cSr/HF919peDI6XOSUrkMxYGURximIdwmwYqKWvb6QISxi0hVlPx4qWNyOvqsQlRZwyx6Cq0KmEzKjMAjcJs/jiwJeRsPue34V71vZi92AecztznHLcCGD1WxOhGkZBpZeiadn4NTlfvvWrLRgaL3MKiGnZyKUNTym67/mdmNeZ41USgjjrKeCmnIatqwqOm9mKlwnxUAmzOXZ6i9IkBttPYYTCEhTF2voo+t8hbc5eAU3xVA6zESdaItqPyOyeLAU6ZWiAIADJfnPcef9C76D3d1lIPWW1fIauSVuseODskFqg5owLozEdoDKnUVRMsKakf6yYrEZd+dgSrKcAsHdwDCyHt6Upo3xNpvjsO07En77FVeB+v81vfZEvWd6xPDgunyAPEsUqJyZVFTox00ICp2Me5pSixwSJywvqGWajp4IKI0PLTLiVdc7RqyhSOI7zhOM47678fcBxnIscxzmh8v9B8rq/dxznOMdxTnIc50Gy/DnHcU6rPHejIxrLa0GAKDagokiJIu3pwohVeSxIaC78W9cWSQkWJYehRFE44UYlRPHFe9y6w53PAoUBV+3LD7iPSZFxbH/BdBOvFEbVKFIyHqYo0nXnXi8oiqLlFgimu44faIy2F0lAa1XTRyZRjKvnOmG2PxmiC+0xYKQTKVmZAAlJccvF9hi1EsVsyuAG0EnaYwAT2+4hLt4eSKbQ1WsWONBHUbTOQd7HTOx1drSC1hbulySfsomVBzf558yr+0Y5BYS95sCYf5/qPZiPDJNQqf+b2V59jaJp2ciX+Nqm3287EFCpxO9dOL0lUVJuGDlww2xI6mlNRFFNUeRa9Si3xxDCbCJCsZrTRiDUhE1uyYipTEWNO++pci2mnrIwm7amVHRNNr0PGplAmyhKdii5LpbVbKT0/WMkYEfl+q98bLFJX5LsuXfAH4+2NmWUrskiFpDEWarqj5PfPhBBFA1d86t76qEoRoFYT8NSV9d2vzt2TLBqeU+yRFRx/WoJs2nqCG9TZqSA1kqWZ4MripPTjKWRQYliVlKjGAbqny4Ohb+uHqCkSVQUGfp5mwzylZk5jjApKoqAn1a6e33wNftfjV5fFvQSR7Ta5/HW0yhFsRTSx5AqZ/Q9AespIdiy0J6A5XZ/Y7S9SAJ6QTpCFcW4m+Oimf5xrcFByiGDRT2daBAoDn5YQqAfZmNV1UcxCjSiPEl7DIaJavdA7ZHLF3SiM5dCa9ZAZy6N5VXYJOsVMtMUUKYaNzmuEdFN6mL/4ZevBAZRKhMr1diI49JqTcvGjv38hJ6M6MnAiOujr/CTf//ngZcDxDVjaFxa3s837Ma2vlFEgU5iRLfHqI+iSLdVVJhNNTb4YJhN+DVG17VATScjijJSKKvLjDvvqWXZCvRR9IliJGJKMNIp0gcx1Hoavv2o02SMTEaobHPlCbIQRZGhNZcNWNY7c/H3hwXTfBJDzzk6qTIoa9cBvzaZEWWRyCvDUDQyVrbBiJOTpq4+aJ2Jf9qzDKuW9+DNx02XfABw8Smz8MSWvmSJqLX0UbRM4A9P+Y9Lo4FWHhxYneKUotjgYEQx2+4emJyiGHHD4MJsRoJTHvVEmKLYMsP/u+9l/j2FClHkiHBMjSLgkiiaVqpajyhi/U/ie1XmungyLqtRZIR12+P+cy+u8U88qpyJ+4Qh28bbT1R+0/hBv5D/vE/7yzsWTG7biyQ4CmoU2c1x5Un+sU/VCYfcUQzYMChRNDKJlCxxEMhISJZYT+upKAK+lQsIWk3bW5oRV+o0ke0emDK35uPnY8MX34lN/+cSbPjixVhThUIXNwusYmE1LRsPvMDnmt2/YfeE1moeSTAtG999fKv3eKRoBgZRKhMr1fSqFBVFOmhlRE/83H94cLNS3L0qcWXBKvRo2b5/LHSgzEAnMcKUJ8u2+RrFuimK4b+9Ghu8SAzjJlrE2m5G7GQ1irLrQdx5f/Yif7I4GGbj7pfWbIwiJSqKAsJrFImiqFijSBVFFRVXeYJMaI8BAH1D/sRJa869v1O3xLrPXxyrMM7vIooi+Y00XGhQlsIKvzY5XdmvpaoVRUWiWJns3uHM5Pr1stTVT5Q/jZ1DJRi6hneexqfW6xrw9VVLcOHimVwwD0WoA6daosjGzb/7jr/MLPIuOxGtlf011q8W3nOIMEUUKVEEBEUxgihS66ljR6uPtSJMUaRhNvsEoihTFFnqqWUCg2/Iv6ucj08rVcHQTtKr8s/85V2L/GLfcp5vQSKmnlLCSgnvLz7pn3jKiiK1nioMpscr/n0jBcxd6i9fefPkt71QBb1BHuaKYlQD3ZShYw7pv8eK8gHAJklsOhwYnPU0lUjJEmsUmyVhNnRwUWuYDcAnn4phNm9dPBdLF3SJb+HQCO0eVFBryAwjE3/zMz4M4RZJ4MgU5Lhv/S78+jV5YAQbRKlMrFRjIxaVLKpU1Rp0pEpco74nCnQSI0x5shxecTHqlnqqZj2tvj1G9CCe1ilmU7qndsqIoowcx5335xKiaNZDUZQM8ul1mm4zWv8Z5Q6hv3W0QBXF+DFBHFHuHy3ivK89ht5Bd8znEEWxb9g/j9qagxPBVGFcOC0YSDO9NcNNyNB9T4lilPUUANIppijWwXrafZz8NSdf5hFKEymuXy8NU2L3u+eEWk3bAVaeNBOr10UHDkodOOIxI0y6h41N7A13hI+bX/kFrA13Bt63rUDEm1E5oW0EHIowm8YCIyBMYVO1noqJmMVhvnaunuAUxRDr6QF/ZhiA/7sYYdIM1zvNyNdrv5J/V3EkPq1UBSzoxUgB00/0l1/6deAXn3bJWmnMt55qBm+NNYvRhJXZW+n2CCWK7TzZVlIUyQBq3C/65sh5o+EI6aMoa53ALCMssIXWskwnPdsoudJhw7B5RfGaFd3K7RLEwVYrs54afsPmIkkZrEcvQ05RdMR+YOnDot2DCmoNmUkSODIFOVQIVVx4FGs1kzRMImgZ9ocitQYdqRLXuO+RQZzEiFIUrbrVKCYPs1Fvj5HMuk1JGrWJipNqgNyOGnfe/4qc01RRtGzHIzNiUm0A3H0wWlEshSiK0amn/u8apWE2CopiVCscANhxwB0PjWYcQAcGRgtot2ykDJ1TFDMpuarKFMYl8zpw6a2/4Z6b382TR54ouvdJy3a4eyvFb17rx0mz2zxLcV3CbE54B7BpyA8TnP0m4OyPumU9Xz/W/Z6I/serlvfg7mffwP+8GKzx29Y3Wl0tvKh4EuIYNTZZ3vmvCKG9AIDtj/wrbhrs5t73i5SNz7CvG9nbeAGJFUwRRRZEw4gitWeqhtmwz2mfW991Y+AURXKS0Zo+R5hp9Kyn7Pe1u8buOLXwtYfj00pVQINeaKFu22yfjJfH/G2caXF/m6a7Cq1ZiCes63/CW1vNiDCbgp+mpqYoHnSjqXWdT0RtaWCiqB8ZiqIKCWD2mExK52a5eUVRsJ7q6diedXQQGOijWBlE0eUsYCFbp6TRruZwRRFGJtH6NzpqSfqsV2rq0QyVQdRn3n5i/MSEg8STF6L1tJUoWbUGHamQW5Xv6cylcPzMtshJjDByYFp8e4zaahQnTlEUa0Nz6eghYSshaTSlWVaPGNY7Muq8p9uJWshHudYYMdZTTlGU1CiGJHcWq1AUuTAbhXsAJcpf/PlLyJctGLoWsMvblWt/qVz2Jr36R0jNboyj6bgZrUgbGvf7qO0UAHLknGMkfKRQDq2i+tqDm/H8G4MeUa4+zIYcY7bFK4zv/R4w+7TKc+62bW9uBiQiJ6s/DLOWvrpvRPlawCEi9TRqbNKU3wtEnOa5fJDM7nOIQ6iBA22ObqJomUCpojxJFcUo66kw6zKRLTIshTAbEaL1lKl1ceRr62PxbSH0tNsSIwxi0AtHFOcAmcoFqzTuW08zLS6RTTW5gTpmMZ6wDu0UFEXFGkUVouhYbkhRrotXFyk5bzQcIdZTFRLAbmyduTQ3c22Bt57qDm89TaJkiXaqVi/1lPTRqgxg6lGf6P6e8BpFGJnDpt3DRKPuvbOOQqgMolYt78EPfr0N2/qDk6Z0YiLp5EWUklXV4I5AVXWP+57jZ7bh3hvOi/yuqNRTq259FEmYTWSNYrJWDex1uuZa9QAVRdEf1FOimJZ8XzXXInrNpdZG1hoDQCBQJ4CYGkV6raZkp6C4/TjrKQ2zUdzmjCjf9ewbWP/GoPQ1bJLQgGtzfOcps1EslgE23NGi91MmpeP4mW14hbSHWRCpKLq/fSCkPpHhoZf2orvF3aZVE0UqdthlfjxGx3CVMfYJc7swzczgwKjLFhd0N+PGC4+HbTu45b4XQ7/mkVf2VefAieijGDU22eNMwzztQOjzu53g2JEnio0baHN0E0Wufi+p9TSifUO9QUlpKqRGUYSoKDKiGEe+xvYD538a6H0m/DXdC4GuY4P21XQL8K5vuCSRzngxoqinXaKVZoriuG89ZZHCqWyFKBbU+hhSQkQvOJGpp5L2GDKMHXCJ4uGiKHKpp4ev9VSFBDCbp9jbyeaspw50h5w7Buv5paZkBRRFwXoKTABR5BRFYTBQmew4VD33Ggm1kokpqBEqQ9cwtzPnEcXZ7Vn0dDUHJiaSTl5QRTGb0rnen7Xaq1VV93rYuEP7KDqOUKNYpzCbCOupah9ACst2kDZ0772fvPN5fPDsY0L7X1KSRie10pLXyuyocaCEmqphI5yimCT1VGY9Jd9BthmnKCpaT7kwm4T3AHY/kYVvsfuYDhu7BsbxH0+9Dh3++m3cPYJTbSfyuDplTnskUaTtMfIeUZTXJ1KMV36zKVlv07Jx3/pduPu5XuwZzGNOZw7XrJjPH0+comjyTjD2t+N4YoRupGGT77r27AW4esV8rPr+05Hr+eLOIfz4w2fjoU178fiWvsDzoQ6ciDCbqLHJ3dZKrNBfhePIO2LcY60MLJtSFA8HiD0UAT71tKgYZgNMbC9Fznoq6aMog6gosiCbOPLV1OGmlb72cLhFNdflEkURXce4QS8i2ExJ22zXyukpimO+asuWMeJnFoCzPhZNWM+4TgiziVAU6cmvmuQ6fgDA8STYJssfH42GI8R6qkICNu91929nc5qr/7EE66lu89bTJBDtpK0s9ZRYwUaL7s2sHq0xAL7uR+yjmLjx7xGMI6VW81BChVDZtuM1QT9xVit+9dkLpJ+VdPKCEixRIarVXq2qutfDxq2sKNZCFMn1plBH6ymrt6IEc+POIdy0c6NXC54SFD6q1D+346AbzrG8J7QeMSnCrKcjRFGMtZ4mUBRZjaJp2XjyVb9n9Pef2AY4kBLmWvooUuQielZSRTFv2vj241txlubvp19u6sP3zPWBfcRgCkFrALCtfxQWIZfNktTToRhFEfAJYllQt1WyBVKGzhNFqywniiTt1dFTGCYTBcyGHDehPFY0Yega/nzlIo4oLpregj9feVy4AycizCZqbLLGeisu1J/HpcbawHO/z56PNYW3Bpb3cUSxccNsju7UU44oJkw9lYXZTBTCwmxSmfA2F4Uh4Lkf+urZvpcqLSs+EP1d85b5aaWnXekvn77YVyXNIk+iWVuLMALGZkpYzxhvGztuLSDgEzD2+8yiWh9DpTCbKqyngE8QmaLYMj28eWoj4AgJs1FpoMtqAztyGe7GTa2nhmZDoxZpcaYwBqL11FcUg+ES9VIU+dTToPV0Ci5qTU2dgk+orj17gbfsmO5mfOPK03Hr+5ZizbqduOy7T3mDtI5cum6tR2i6plgnJ/aHm9vRhBUJe3XStgFPf+4i3Ctp4VKP7wkLjQnWKFZ/fWii1tM69lFMki7LSAAlU/tHS14rlSQ1ilHg6gdJGNBoMYmiSL43tj2G4/22u0gC5vb9Y6G99igx48JsFAOEGMQ6XQqLKIqsHt8gRNGCHpoAzH7PAxt5heoHv36d+z2yPooqiiK715WFa4Hy8UTvw6Jrj032k/u2paW4685woYx71vZiqBBNai0HeO9tv8Xq53hh5M8vOC5wLaBJpnevF5Q9cgxFjU1s6Lix/CmvlcdeTAfmnwNcfht6L/oe53ZiGESLP7n94mrgPy6O7rt4iDClKDIwwpXK+jV4pTG3jvGFO93avqFdriK37LpDpChqwUSm5un872AY3w88QPr/FQbdNhOL3+3+2/yA/Lumn+T+L6aVXv5dYM2HXUJsFv3aTsBVNvMHg3ZcwLWWsvVjRDFNyDi7IKQliiIjrI99GXj6Vnd5xwK3RQWzt8a1x0i3uK+rSlHcz//fyPWJwBFToxg303/R4pneY9d6ShVF/2Kc1rWaiKJI/lokNYphr60WnZGK4tF9uaaYqtWsDxihuuMZt13SZW+aiyuWzQsoAwCw9g8D+MRPw1WMJMhl/PeLRJGu10Tbq2v9nrShQdOCbZRtx6lf6qlimE3SPopJAqHiSMDi2W2B5dXUZaa4wXuV1lPuPhgXZmMnTlCun/XUX8+VJ87AE4SEM1KRIuSQWk/Z87LQLtXfk6uiRhEAprdm8cbB8UCNovLxRJ09dEIf8MdmpNyqZPPb9bFX+vDj3+2IXU8A2NA7iA29fB1ovsyfQ6IS+o6UxjEjU0t5D+NSay0YWG2txGprJVYc04V7P+LWOF9hO3hsywHufQYsfDv9XRha5Ti3iq6DrvcZ19F35Q8b5p5/dCuKshpFwFe8SqN+H7/eZ1zLZu8z7mOx8HQywmxS2aCiFVYz54TMPG5+ADjhYrdp/PxzgPYeYNbp/vNhyaFNHYTE5X1FUU/5aqyMgIlBNoBvM6XIkBpFwCedRspVORlW3sL3MQxVFFltZuUGRi9OSRRF2/aVxUauTwQE6+nhqygyEvDZd5zALb/+vGNw2wfOwAi5OXc2p7lBK7WeZnWbr+9NqMgFiWIw9dR7bZ1STztzU4qiKlRUoynEY36XX8/ZOzBecx/DOJiWjd9vO+h/58Fxr0fq4QZN0ziCwI49065f6qlqmE2Jq1GMV7eSBELFkYC9EjteNeRYFmZjWjaeINbB7zy2Nfp4iemjyNUoWrZy302GTJ2sp9R+/fl3n4w3zffHoJm0ex8wQI4h8IoiIA/tUv09lKiOVYjiYIyieMmpszG73R0HmkJ7DOXjyYgiikxR9Ler2B5jV41BZbRnJBAk1mKI3C9f8a9Vhq7hX655k9K+Xji9xTtG2ZimhWzz63JP41JjLaRHMWv/1iA4uomiTFEEfBvk0K7wOj2RFE2oolg5eWUDRTHQRqWGbsMdLtn6yMPAX7wEXPNj8l3kd9GWEk0dPInz2lq0EgIpURQpoQ5YTyXrTRVFBq6PoaDqpUhoRRRRNEKIoli3RhXb8QNAfsAn3Y3cQxEQrKeHr6IIuCTgosWzuGWz2ptg6JpnxQEqqae0roVc0tKGxhNFUY2PgWg9ZcqHbOY4qe0oDF2RfRSniOIU6o/ulow3aOw9OJ544JwEbPb+9t+87i0bLZqhNr9Gh2nZ0CXlCKZl1zH1lNQoluWKomnZeHqbH7r2z49siSXfc2ICn2ggVBwJkNVO1lqjaNq+LfS/N+z2lm/tH40+XuJqFGkfRdNOnKDMW0/royiWLAfTW/zJ3Z5p7niIqoi6hCjKQrtUf09zOmg9pffWWy5dLLVkp1NaZZ35ba98PNFxiljeJVEUi3Z9aUqeJNUCQWJddvhxwpoN/dzjV/tGvUkZMSCIYvW6ndwxOla0PEIOANemnwQQ0VFjfR36mdcJU0SRQaYojodH3QYgzozUE0xRlA0UWwTiNO34+M8bEsJs0uRgp4mgYg0nI2VmwT/Bs22EQMYpipU+k2kJUUyHKIqAX8cISIgiUc7KEutpHFEU1c1Ov14HYwf41hiNrigeITWKDOIgZ+s+95gbJg2BO5rT3OyeSd6S1uBbm/VU4vrSgKKYCSeK9bKetufS3moG+ygms85OYQoq0DTN67HWO5Cf0NYjE61WTiYYiaEKBbtm7RsucgpfNfV6DFzqqURRZOuxZr2/7bb1h9fYMcTVgtNAqDgSQGtOGcSJNhVQtc+0neqOlxhnDV2vkmUnIsziOia1+1KI7SnGCIGxSZgNgyGxnspCu1R/j9x66iuKHzh7gdStwbafeFzFHU/9o0V38sKBP0EvBkZKahRLdn0mYRlERVG83okKZu9QJeW1Usf48Z+u9547/7hpuHL5vNDvosfo9gN8PWaX2S97iw9xnH4IMUUUGWREMapXoIiJtJ4yRVE2+BdVLlpXGIYOIeghTS4slEQxldTIuAoV+/5ywT/BMy2+emWbwSJcqaIos55WtjlVFFnhB9eeQiSKMTWKMuupSX6jSFo7j/H/Hj/Af3ej1ygeIamnDKbN34he63OPucG8fzPryKW5QZhNrKcZg1hPq1DjREIYaT2tE1E0dA0dFftpoI9iwtTWKUxBFfO73XtA/0gRs9qjrx21tB6ZSLVyshFFYvJlCxt3+uOLWqynKUP3XBMyolgt+U4SCBVHAhbNCE7+VvOb6bW8GlsoLBN4lbTt2vpoIByE9nwsm3YiwgyElxkkdZVQ62mhbHEEhrbHOG+hGxbIE0UtNLRL9fdkUv5xxer2mKKY0rXQfpVsH9kOP5kbdTwBwI4D497khcMmPRVqFAv1VhQFVV4k1mUhumVGZ5s3GXPTmo3YOeCPH+9c24tHXw623qBgx+j2/Twp7tNihAdxnH4IMUUUGViiJwBkmX0zQc2ELFCmXrAirKctM/jHKkTxjOv4x6FEsfKbmjpcNYaSOBZmk2kNb3oPyGsU01FEkXwW+91h1lPLBLY86D9+cbV7UyiN+++VKoqEUIo22OZuoKnT/94pRfGQQayB2NY/Ctt2eOtpc4azdfFhNvBvOFWQrID1VNJHkaFe7TEAv04xqChOWU+nMDHo6fKvyReSsCgZamk9MpFq5WQjjsRs3OmXbtQSZgP415eixHpaLflOkvoaRypPmBkseanmN4vtMRIdL5bpZko89S1/2Vi/mymx+nr3eYg1ik7iBOWwIKfkiqJPSMZLFlfvSBMyrznL/X4dlJQtCE3mTfJ7mKooKoqdzRmvV7GITIqv8WQwdA3fvPL0cCtlBQ+9tBdlVlZRCiGKpEax3kRxTFAURWJNiaLp6LjyrGMjJ2MG89GCEjtGt/fziuIDxkXRKyqO0w8hGiNS51CBUxQ7/b9ZvZwYZRaFyQqz4ZabQP9mftlQzIwsaytBYaRda55tyq2njESn2Uyz4/dpzLYGVT2qGHJEkSmKkjpKMfWUfVYq6xNFPeWvC7sp0BrS0X3uTeGV+/1l7PW0Ps2MsJ5m211CWBh0SSKnKDY6UTzSFEX+/BsvWdg9lMcQtZ7m0jg45k9OmOQ25RJFNsmSnCiKhLB5EhRFoJJ8emA8qChOWU8w2fHWAAAgAElEQVSnMEGYT2ptTp7bjktOnV1Tf8EwqPRIPVwQR2KGSXx/rQFL2bSBsZKFgkRRrIV8q6a+xqUMf/kXL0k/OynE1hWJjpcX7gzPlGDhIMs+yE3qlSzb+20X/NMT2DWQh64BZyzoCk1QTofUmybvo+i/Ph+iKAJAb0WJoorimYtmACHHVJJE6JZMCiMF0/tuNgnb1Rx+rxFVX6qMbu0fVZJX8pYG6bSnRFHMW/x2NXRNWnv7zlNmARrw8EvR/Qh/t3U/TMv2js9Vy3vwq5f24tHNrjJI77uWnsGqM3pw9Q9+p/Cr5GDH6PYD49zyNdZbcfPJO+THrGycfggxRRQZmoiiSFWmEy8BXn1I4bMmOcxGRpQAYP2Pgu/XDKDnTHeGgrWVEJFudskuVRQZ+fVah1DyUTlRYxXFymAjlfM/J9J6KvksRhSbp/l1ZlE3hdeI9SSuRlG0nja1u99zYKtbG0nVTFG9bTQcQWE2QJAoAsB7vvMUNwDpzKX5pDwyjkrrjm8fr4Yokht/2tA8a5FsQFBPRZHdpIOpp1NEcQoTA5p8unswj+9euwyr/vVpvNDr3iPf1NOBD5xzTM2tR65ZMR/rdgyEPl+LWjnZiCMxrdm012evljAbIFpRnCzyHUUqZfWIVVlPaY2iZSc7Xp6PCf9Y/xNg2QcD7THc79U93rVwegvuveG80I8Jq71Meg/ICWEyYYriH/rdc5CG2XCTwhKoTgCwOkk/zIYpiuH3Gj6Zlr9Hv7RbbRxcdkK2lST1NG/xx5FlO7j+vGPwo6fdFhkLuptx44XHY9UZPXAcB/c9vwvf+9+t+INAzBj2jRS5lieGruFjFyzyiGKJ0KJMNgtN12InY6LAjlHRejpadtwWGHe+D9j6iLtw9unA2R8LH6cfIhzl1tPKQZ1u8QdhlgkM+wlbGN4LnHy5/1gzgEu/HvysyQizoSQqiiiJOPbNbsIpbSshgtlPmaLoOLz1VPx+hoxEUQTc7bj+J8Cu5ypP2MDz/+XWCkRaTyUppizMhtpO424KDB5RJCTbjLCeZlp9IlkcBp79N/+5RreeHmk1ipIQhoPjZfSN+JMRrVmDm+HkUk81eHajaqynlBBSm5CsFqVe7TFMy/YU02AfxSnr6RQmBlRR7D04jpSho1RJhurIpfHfnzi/Lq1Hktr8GhlxtWAnkd6CtVpPmWpTkiiKSWvsJgIy9bCq9hg6nfRLaAsdiglCqoSDyIgi4KtptJetdB3rZj3lw2TCFMU3DrgEoyVNtmedSASzno6VLJRM27NlRm0Dsb0Ixct71IiiE3Y/9qyn4YoiACyc5o/bbrrkJO/axAjytNbo0hvRjr2N2EJp2rhmuJ8TFxAURqzZMeo4TsB6Ol6yYGsGMJe0f3vPd6LH6YcIRzlRFIgQU+l2/NZ/zd4NwCs/9x9rGk8cGSYjzMYgB78qUQKArmPjX+MRxQpJKo/7szpMbU1JTpasRFFk2/H+G/0ZIrPo1wrISEyYoug48ob3cTcFho13u4RVCznURaL48s+BvRv9x2OkUJnakxsRR1qNokJftUc39wlJedR66tRkPaUDAlrYL1UU07Vf2FnB/Po3XFu3qCha2tFtAJnCxGFOh39N/snvduCK7/0Wr+1zJz+Pn9kaWq+UFEnq4hodUSTG0DUu4MWoIfUUIIqihCg2AvmW2TGrao8hpJ4auobvvH+pRzozhh5+vHSEp0+6z/dU1pW3twLutXek4I53OnLR94ow62liRZEQxaF8mbvf0QnPN/a75+HsNrJeYeOZhPAVRYvroRhlPeW2n+1vv3vW9uLnleCkuD2fawoZn7DxouUrimNm8NNooExnLkhqk9qxt+zzhR4uzKYyjoqbjLn5ksX4uz862Xs8vTXjHaOO4+A/n9oeqI0EKsE6KeoUjO5jeahwdI88RKKootKJdXwMVskNSUlHKDmW6X7H8z9xiU7HPGDZdcDSa6NnELz2GOTkVSVKgCJRrMwoM6JIrbRxiqKo1sXVCsw6Lfz7RXWyPO7PMlGi2DEPGFaIDx7qdQnqll/KnxeJ4r5N4Z+15X8aqsCYg2UCo4TU3vth4Mw/jT+2GhiirUWGe5/biU9edIL3mL4lraMm62mWUxT9bShTD+uhKAYa/wr2nCe3DeFChe43U5hCEpiWjVvWvOg9Lpi2N1kBAIumS9oZ1QBVW1yjQ1YLNlI0MVIwkdI12LSPYp3CbGR9FNl6XHP77z2b5tKeDlxbB6uwKmQqWzXtMeh2Yo6SkuV4JOodp87CbdeeIX/zsuuA3mfCP7xy7xb7KALAcMEnJp2xRDFEUUz4e3NkcnH/KF+yQxXF8WIJQAYzW9MAcy/WTVF0KcB4ycRdz/oq25Ov9uOetb1YtTx4/FB7cNm0vQlOeu+KunNfcupstAzmAJkzVKIoxhJFCalNasd+NYwoVsa2q5b34H9e3IMnXg22tLjk1Nm4esV8lC0bX/3lKwCAsxZ24+oV86XbhmI4X0aLEVG61SCYUhQBXzFTVeloI3qKKFWRqmy9z7gkp/eZQCJXALbtK3uUqMXNnlF0L4x/jWg9lbUOkSqBEkUxbjtufjC4TGyPwT4rLPF0WULCtkXynYDcBhuG5/8r2XdOFtixtX+Lv2zPhvhja5LAZhtXff9pnPe1x7Dq+0/HNoMGgu0xZNg9mEea3MjKXOqpQ9pjJCOKpmXj8c0+8d49mPfWeaLCbMT0QlFRfGRzgr6uU5iCIqIS/QBgPKTJ+xR80sv6zS2d77pOLNvhFKJayJpp2R6ROThWkl4/U4aO2UQVvvOj59bFKqwKWQ1mNV8tU/sOjvkqS3eULXTptW4IiAwkHCQtSe0cEnrzqq4jQ8bQE6vuVFE8MMorSVRRZCE2szhFsT5EsblCVm0H+OdHX/WW7xsuhvbhzHCZAHbs9YPhtLntnsqmhd2PPUWREMVycLv2Dvgss6sleEwktWNv2evXD3J9FCtE0dA1vHfZXG9xe1MqoGxnU7qnNg/n3fM1btv8bMMuIel/iig2Fmw7GNaiqtKFBddEBdqoJHLJQKVoqtwlIUpVKYq0dUhl+8jUUlnqadx2HJWkUkmtp4Vwohh1U0gCUVGMQv+WYJ/IRkC1x9YkgPYfWrdjALuHCli3YyC2GTSAWCIJuDODtC+WSd6SqrI9Blvnf37Ev3GOlSxvnWWcsB5EUbTLiDWKe0Yb8NibwmGPuPYKW/ZOYP39EQamiJm2wxO5KgkbuxZt3+/WNzlA6PWzVEPz91ohqmkpXavKrkw3k1WZKKRN4GWkwH+z4YaDXH4bMP8coL3H/f/y24CrfuSpcGmJokhtlzIrI4WhawESXE2YGXWpiIqiRfoBGxV9biRPFLI6KYp0HWSQ9eGkkwIl04m9fjB85b2n+ZMXeoiZUdIeY1SiKPYe9ImiTAGOsmOnDc2zY5uWjf98aru3/Q1dE6yn/rGwbocvEP30T8/BvTecx03GaJqG9iZ3XUYqicdx2+aBF3YLjrzGtJ4evUSxOAxPIGdEUVWloySKesWLEb0U41S2X39LTkLoDAMlUVFESVT+EtUojrt1gUWZ9VRBUbRKCrUCkufDrKdhRJHeFGqpHUxCFAsDDaHQBaCS9naIUG0zaEDNenr1mfO5QRgVP9IaVRTVg2Di1vnXr+0PLK9H6qlYMC8qil3tkrYyU5hCjYht85BvzMFLI4LWIpaEHnPVIMn1k9UvpnRt0ms9RSJcbcqrpmmeKsMUWV5RjJnwM1JuGMhHHgb+4iVpiJ8szIb2wotK/GQQw3uqIebNJPV0v6Ao7hjwx30s7fS57WQsVCdFMRdDFIFg8EtaUBRVE0GpahtOFIOK4mgpOA5gCnvG0KVkV1YL3dbkfqemuecHm4T58gMve++zbEewnmY9R9S969xSJ10DNu0akk5kt1dIK1u/uG2zb7g4pSg2LCwTWEfaSGx/0h1ML/2A2vspUcx1+39HJZ/GqWwDr8tJCJ1hoF7mqNmz7kX+65o6gVxX9HcDPlEEXIKmaj2VKYpxaufy64PLwsJsxkKIIuDfFE68JPr7GGQF4Emsp8AhV+ikUEx7OxSothk0EB9mM7u9CavO6OFuXEUSZpOqsj1G3Do/vCk4cKvHDL5olxH7KF685PBJhJzC4YO4RL9c2lBS96fAEyYaPJOqMswmyfWTtc6oZ6seVaRToqJY/Tqw9zISp6woKiIjsbcOjScjiqKCWs02byJ9FEVFcbDgz3gy6ynto4gaw5EYWrLxMSVi8ItItOOuHwwcUQy1nkpqFCWppwydzelQ5Vq0hZ+90B0/lkwblu2ETsKUSeqpbWQ8R1S+cn7ZDvC5n70odUQxMjpc+a1x26a7JTOlKDYkWD3Xo1/wl432ufVcr/0KWPzu+M+gNYq0t16U9VRFrZSREE5RFC6SYbNnlBiqqIkAT5jKef43eqmnKu0xivG1Aks/ECSd0hpFQVFsEYgigwoRBuSqkqgonvjO+M85hAqdFIppb0lQbV2hiLgZta19I6HfQWsUP3z+sYG0uQsXz6xEYvvLC8R76rbHYNZT9dyuuHXuHwnO+tUjzEa0y1jC5fniJYd3+McUGhNx9Tw7DuZjbeJTcEGVvGKZKIpVKmxJ0hsZMZ1s2ynAt7UAaqvJZNdz06tR9EnDtJba07zpfYSpvtR6Gpd6Kn4GUN02zxi6t51GCrxAwNUoahKiWC9FUSGtWwx+4dtjOLHXDwZeUYwhikRRZO0qpktaXnTFtDKh4NuRmKGTMCZRFPvG7ESOKN96asJx4rfN2Yu6pxTFhkRUPdfmB4ATLgbe8lf+svYe4JT38q+jahvtrRcVZqNaUyiSEJqCZChcJC2TX4/BN9zPjKuto4piOR+SeiqZHQmE2RR8tfOsj/nLuxbytQKikudZT8lnlSOspxQ5Yj099s3y15x8WUj/RsHOd9VP4onnBCh0NRGzuGMrYVJrLXWFIuJm1AbzZuh3UOvphYtn4aLFs7j3drW4F2U6w0mJIq8oqt9Q4taZhkYw1GNwJtplWnP89xjpw7/lyRQaD1H1PAxxNvEpuOCIounfc6utUYy7FtFBPKu3k/V5nWjQgBggvIWECmidJwAMjFFFMXl6tQhD18BEKLn1NP5eUQ/rqaZpoUSNpp4y66nOKYqTU6MIBINfREVx1fIevPOUWeLbAABnL/Qdd1S1hRFjPSU1isxZM68zeN+NCx6ioNs6X7akkzAGLFygb/AetxzYiKuMJ/htTyA6otpz7u8qWTaKph17bV0yr5Mf10+lnjYI4uq5NtwBLLnSf7xkFTDjJP41lESVSBPNJ78eTsqWXuuS0DiIJISG2YiKogimlu71o86RP6iWfhlQFGXWU8lAVWY9BdwLwTHn+ssvuImvFaBKXirnL1etUaSgNYrnfgroPMb9W9P5YnaZ3SFDfnemFUhngeknyr+HoQqFLgo1EzPFtDdV1FJXKEJ1tlH2HVRRNHQNSxfwtag/37Ab96zthU6sJ8SxgxPGn/cf7HxWbcJEYZ1XLQ/u/3oNzqhd5pY/EtrIJCC7U5iCKtgExTHTom34UTbxKbighLDEWU+rI05J0hsZMc2mJ39YJ1pNa1MU3c9i1/8DtEaxDtZTTdM8B4hHFKn1VEFRDFpPq7v+h9UIylJPJ0JRjCOKsj6clCSbltvr8m9JD8Fc2vASQb9yuX8Pq1ZRZDWDorIJRPd7FEG3db5kBSZhDFj4bvrb+Gz6Pm9ZmzOKb6Zvx23pW2EgOHYQbbltWX99hvNl79o6v8v9Ll0DTpjpixOFw6SP4tFHFFXquSiJKY4G+yZSErV7vf/34BvhpEw3gAtuiV8/kYRwimLMRbKW9EtOURznVckss57Kwmxa+AOdrq8ZktjK3uf9TQYoYo3iOAkOofWgFFQBLI3Amy7sPo4vZpddnHY87f9tFdVqVevcS7FmYqaY9qaKWuoKRaxa3oOzjw3ZbzHfQWsUNTj4X9KuAnB7Kd20ZiO+dP9L3rIiOe2WDT7iP8gPKLcLiWtgfdXy4OBtQuqCRLtsAvvsFKaQBClDR1nSzJ1CHBRNIQg9pEaxWuIUdy2ig/iipygeAutpIPW0+nVg7Y6Yo4RTFBNYDaPgEcWKA2UocZhN7dZTIJyoWRJFkSeK9dnHrI8iRcbQAq0fxOcZmHX3jYP+teGTFx3vJYLSmtLBJGE2UkVRRhSrtZ5agUmYK4zf4FJjLRzBxOU4wKXGWlxh/CbwmSJ5ZYoi4AfapAzdOz56uprxtSuWeK8ZK5lTimJDQqWei9oRS2NASSSKIX0UGcJI2cie+PWbdhyvelhUro+xntWSfhmwnkoURVl7jEybXFEEwhNbAV7BTFN1UVQUD/qvz4TMeFPraX7Af0+zQFBkiuLvvkvWtxxfq1qFQheHuhAzhbQ3VSSpi4ldLV3D+87iL8hL53fGztruHszDItbTp7YewDPbD0pf+yRpgpsn1lOpaVchjEiWmEZvnE2SGfsJqQsSb6ZTiuIUJhBJbI5TkEMWZmNU2SqCvfe71y7DR97s90Ke35WTDuJLh7JGUSBO1aaeuu9limKlRrFSP9icMdCkUFOnAha+I9YoahrQ1qRSo1h7mA0QXiPYnPWv9cYkW08vXTIn0PqBgk4CMDK/fb/fh3DRdH88R+s9+TAb9fYYjCjKrk8qNmEGkSiKkzDXGE8A8HUGBvb46srzFKItlx47wwWS3FqZwW7NptBMyPl4cUpRbEyo1HNxRDFGUQyDjJQNK1j2NvyUVz2iwmxE1JJ+yVlPx/3fqOn+9ghNPQ2ZEYmqr+QURUoURUWxYj1tJrWgIqj1dLTPV0MDKamKNgVWq1onhS4O9SRm9YDqgFG1rnKfEP5y+3XLcdzM6FYPcztzKJPPefQVSe9NCWgmQOhQRSGMSExMozdOTdMCg7GJURSF42yKKE5hApG0SfUUguCJmxVYVg1Sho7Ll/rNvv/4/7H35uFyXPWZ8FtLb7fvpn27smwZr7Jla7Fx7MExS8AEiIlky8YQ4AsJ+YgdyDCJE7LMF5IBEpNhBmLiCV/IhHgAW7ZEsAFjwMbBYLCFZFu2LHmVtV1JV7q6++2tlvmj6lSdc+rU0t3Vy7XO+zx6dLu7uru6uvrUec/7/t7fuhXCSXylkzWKWorWU09RdFNPXUUxLTUR8IktsZ4SEtOfzyTad/7zNkrOw6yniwf8OZEGC305HdeuWexv0ML2GHELQnTCLTl+r5zwS7BWL/Kv7Vndb1+RzHoabI9Rc4nickE2QBL1l4BeZCjXzIDlfpkyGvZU5/25x0W23P48pShSn3em4owFvTkdxZy/H3NFUTz9vEyX3gy89JDYoknUImJTtGqOoshP2JIQRREpm6CUofN+HXjhe+LnEtVj3QfqC7MZWAFMRpDBqNo6mgTSYTa5Pj+KmVcFFc15Hp96SkCvjvAkjSamjPWUeq0jO4HRl52/Z0ddW+jNwe+Dtp6eetX/m1cU62i6jqe/4atyLcaywQKGJ8qhj2d1FaZlt6031o0bV2LngbHQx09MV/DNJw7i0RdH8NAen8CR2spH9o3gjpvXeSvDPNEt1cxE7/HcYf93NjqdbAAtxdjnAKQSRpTT1NY3uA5YT9s/AZQ4fbB5wxAe2TcitMGLJkUSQWgCRbHR+kQadG3eqVmx6uDVKHZAUeQVxGY+s+6RONd66n7eNOoTCTJ8jaI7qU9KPAKppw2mXoeH2fivr8LCpWcM4qJlvcCL5M50vuMewfvHEkWmb7Fz/PafdIiioiBQ6zxQyGC2anJhNhE1irbNtMcwbOc6OL+YRSGjeW0qgPpqFBklr+q8hq6pGCxkcADAMXsBVkSQxWF7AbKairVDA9hy2UpsXj8UmJP1UwoqSbK1LNtXFPMiRZE63lJR7BKQeq5VV/n3LVsXVIuIyiW0niYgiiJSRk9Qp2MUEqJ61BNm00z6ZZiiSGynQDD1NNvrjAxhiiKz7w0oii//0Lcg1GbCa8xo6+mpV/y/RX0Xk6KNvQfjVvJfG51tazx9XFLXgdFZfOpbzzIkkQZfVzk8zpLgUi1o+xC9xyMv+DWJi/oEarYA5ejyQwcphBHxgREtJ4paNuiJkZBIEXGW63Y3cZ+LEBHFNI4bQxRngpNJ27Y7bD1Nv4+iYVmwLBtjLsFIo4ciASF25JgREpMkyAYQWE8btMQmqVHUYDnEmy5JSklRFPVRFKWL0mBTTx0y/6prPR2aVwgo2sR+mkhRBJx5o0BR7C9k0Jtn97dx66k/USCfYat5DQAIaxTJ49detDTSliuyns5SxLYoVBRDMj6SwDQcrvDVtwNfWOP8nzC0rx6cfkQRcAhDHzVJ/dD9wXouYrds1HoqImWEfGT7gKkYoki2rUdRbCb9MqxGkSGK3PvnBJZUukaRCbPhiWKCGkURRDVmtPV0NIIo1qMoppxsGoXNG4bw5vMWRW7Tznh6MmH8wBVnNPwadF3l0QlOUaz6to8/fNs5iV7vXRcvS7RdbI0ikEoYEb+CnEYfxQB4oigh0WJEWa4l4iFKPU1DUezJ6l5ttIgoGpYN4tTvijCbpmoUneealo2psuGVMsyvQz2Kg68o2rBt21MU+xMTxXQUxbCay5rFEUVNBWxq8p+Su6QR6yn93RqWhXLNxOEx5xq/emGwpERMFMP3/1tPvoK7n9jvv4dLFG+7bzdsjsUlJfZAMPWU/gwA8G1cgwfNy4Q1ig+al2GbebWwlyMN1nrqkNFpavW6N6cjr2vee0yXDdz/nK9i/vC5Q8nbopEuB/ffChx6wnETHnoicWhfPTg9iSJAtbhQgr30AEpRnGZbYABB4sgjjJSRGsKBoeRN0qNUOR7NpF/y7TFInV+OJoociRPVLjKKYkR9ZSYs9TSBcsTXmOlZ//XotNaAoljHZDvlZNMoaKqCT7w1njC1M55e11SsXTEYv2EIaLupyHpK3uOqN0TUnlJ419plkel/ZOCdrdFJqQKkFEYUqFFMKWSBAf17TVpfKyEh0THQqaflGqlRTGeaNd9VT0REkU5Y7USNIk+G06hRrJk2Y7NNU1EkfR+rpoXpik9GkypUQUUx7dRT//gpsB1i2gJFUfT+cUSRJsXlmomv/ORVT3Xbe3QyQHQIUSzVTL+3aMT17DP3P40TE344DiGKzx6ZwMlp9tyv55zg+yh6r+8qisV8FrfWPo4/rn0UO6xzccRegB3Wufjj2kdxS+0TsKBiYV/0+9GK4pSrKE5XaKKoQVUVz/L7zOFx/OV3X/Ien5yeTt4WrZkuB3Xi9KtRJKhMOf/TNXg0iFpWnRE3mie45s+An/+D83qKCvzGP/h1jjTMmp96OjAEXHidw/7DQIgKoygmmCyS9Mt6a+toRbE84ZNhWlHkU089RZG2ntKKYlSYDV2jGGI9DYPIFloYdOypNBq1nrYg2TQOo4KLP492h9rQA1y9IBebUtX0rEMEZWqQnkn4HlldxR03r8P2p45g645DGB4vYflgwasVuOC/fh9Vw0LFhDeqney7AAun9jo3+lcAb/4z8W+zAfBEseWKYj1quISEREcgSj1NQ1EEnEnx8EQZp2ZqgcdaXi8dA/49M81YT0nqqWkxpHh+qmE2fo1ivT0U6ecTNDr+9wjaUwCAYfvnjGM9VQG7BamnGfb9+3I6+mNSX+k+ivftPIwXj/ukbmSqgtu27WZyCvjk08V9Ia3KXORQRYbqWUiIogj1hNnwqacENVdRHOzJ4LIz5+PePdfgXteGyiNWUWTaYwSJIrH69uR0zLhzowJFw3JwtiUOsi1RZUlJuhyklLFx+iqKnmLWL36crlGMUhAv2gSc9avO37YFXLRZ/COeHIZnhhtYkdwmatZhPW0GNFGk6yfzUYpiMbhfoTWKdASwAZz0V1Hw4g98X3USRVFkC80L1K8k1tN3/8+2JJvG4fhkvDe93fH0SUmcCCQhkbedAkCp6l/w6AE7CrqmRNriSIG9RQ1p5Txl5934/zTcLkSEAFFsR42ihIREV0OkHqZl2yV1imOz1YAFz1Nq0KEwmxQVRWLrNCyb7aHYijAbw6q7hyIQ/LyNHvMw66lJE0XFcuyejKKYVh9F9v2TzDFo2y1NEmnQpTL0MfWSQCMW7XNKDTqC7TFEGCzUoSiGWU9dRTGjqV6NNl0T/NYL/LTZRbFEMRhmM1NhracAUKT2pQr/OVn452Ksg6yZLgd14jQmipSiKAKxVRpl1s7IQ8uy6ZqlkB6LdGuMgSHWJkpsBNm+IFGh+yjGhdk0A9p6Ok2l3uUpIs1PVrPusdN0/zPEKYrEV73vO+z7EV81ED8IimyhdPIpQZL2GOs/mErvwWZxbDI89ZSg3fH009XGiCKdkMgH2QCs7SMpGY0LRyCrnHRaXMaia2TT/e3w9q7WtMegiaJUFCUkuh0i9bCZej0aZPJqWrZX/0RQqXXWeppmjaLmhdmw1tM0U0/JeF0zbUZRHEiqKKbUHinMemrY/uupJMymBTWKWV1lztnlMUE2QPC7DgMhOvQx9Y51pKJYg04pirUQoljManUt0IpSTwE/zIZejL6JmmsZVD/nOEWxN6t7ZTCEFE+Vg0SR3hcTmrcwkKUIcqyDLKZ87YS2KFmtYwKcvkSR1CjmYxRFIDq8Rs+xJKUUEvlPs/sB9yQkNlHy/IXnBIlKPWE2zYBWFKdookgpiorCKn45qraT3E+riKL6yiS+6iirb5gttCBSFPn2GIJVrJRW5prFSAxR7EQ8fRyJe/uFS7BupX/cc7oaSEgcFimKFFGsR1GMAlnlpBVFzaZ/O+kSxfaE2cgaRQmJuQSRkpa2oggEW2RUOmw9DaaeNqEoqn6Yzeh0a4gi2d+qaXntN4DkNYqB8b/RPophYTa89VRTW1KjCLBKWzJFMdlnJUSHt54CiLye5VBjrae2WH2sJ/EUiKhRtIhF3P9c9HF49og//4+rUVRVxSODkyJF0Q27oZNPAV9VpBXF2N6aUcYAACAASURBVO/C7XLAU0FiNrh95LLU0vK7Y5bcbtg2ZT0NUxSL4vt5aNlookjiax/+tH/f8T3sj56QKFEPlXrCbJoBrSiS3oWAQ+zouF2aKIpqC0MVRfcHlsRXLfqcQ2+MtoXy1lNFY4N4gODgpOpd03KAKIqKAvz5uy7w7l9QzHYsnp40iQWAN1BNdNefMYjbr1+LOz+wASZlgRrsyQQSEo8KFMVyVUwU6dXMi5f3Y+2Q//3FTTwynqLoD2k6oyimS7ToiUFGU5gQi9QgracSEnMKojE6rRpFukbv1AxbqlBlwmw6QRR562kzNYr+a41M+dePdImi/x50j96kNYpB62ljxE2UOgoAJqUoarAc8twCRREAipS6lYQoJlWLyWsNUOetRxRFi/YueEUxzHo6r1jfNZ21nlLWVs966n+uFdRxoOtkFxTj5+CkxlMUZlMUKIoAUHXrFLOKv22sg8wtX+O/DTqlNa20/NOTKFan4fHw0BpFQRKqCFFEkY6vpRXFx7/ExteSSaCoh4qIbLUCtKJIf4bxg2zcLkMUBYoiTRRFJDfGV33iyCsY4UWogZXA7/wg2hbKK4o984MhRfzxixis2g1So7iwN4cPX3mmd/+lKwc7Fk9PWyYuWO7/Tv7lw5dhy8aVUBXglRG/RqEiaHYvsk+wiqL/Hqvm+wsPX3zfOiwf8M/JeOspURSpldgWWk9potgSNRGQ1lMJiTkGESlMLfW0lyaKbKANU6PYYAJnM9BTVBTpsf7fqUnuYy+eSM1KR6tiP3nppPf35x96IVF7At562qiiGGY9pRVFtYWKomFanqIGOOE0cZ8/6fWOEB2x9TSqRrEKXYm3nh4YnU3eSgIRYTZmtKJIMNiTSfQ997mqodcegyKKfblkimIiB5lbvjap+GLXs9YqJqUVSCct//QkiqQ+EYivUYxDwHp6yv87aXytpygKiCLTYqJN1lMRPFsotQ/0sfMURWp/vb8Vf2CI8VW/ZszDrMVOiu3+mFYigIAoLghuw/viuyhJ8rirKC7tzyOjqV4/Ht5i1E4Qy0RWV73BD/CJ3shUBTPUgEvXyRimha07DuHB544GXpetUfT/povea6bNXMBiFUU1WKOomdSiRQuJYktaYwBSUZSQmGNQBQ6VdiiKjPW0VQtXEeDfs7kaRf9vOjH7b767NzUrHb2/j+wb8f5+4fhUovYErbaeGpyiqGsKm3qaQsmMYVq49RtPMS0n9p+cif389He7cp543kgTnUTWU0qAmJe1GUVx+fw+LO3PBYKGpspG8lYScBYHiGrIWk/9GkWCZYJazbj6RAISaBOZesopihWXKPbpVn0OMk33vaYAPlX7XdxrXsM4q9JIyz89iWKZCqdJUqMYCpcAhSmKSWyWAKUoCkiB0TpVhIGqIaTznI9dd7GEMlZRdC9mes63eK4T9yck5/pW8xrvR0Nw0BSQPh689VREFPmkrQ6E1ohQMUzP3rCk3xmMFriD0liCthmtwoyr9vXmdNbf75LDV06wiWcVw4Rt294F6LZtuz2fPo1ZauCkFUWWKFreAK4oiLV2ek2a6RrFFlpPc+1WFLtoUUNCQkIMEUFKK8yGTv3kFUXGetqqhasI8J+xGXJ8RFCuQJCWlS6uzi7ufdJKPQ2znhqcophROUUxhbnL9l1H8P09x4SPRX1++ti9cfUCjwjqqoKNq+YFiM6giCjy1zNq/mZWS0zq6ff+81vwyV87jwkdSrqvPEjKLJnD2LbtKZL05+rPZzz1j2Bhb7L5N1nkn62aMEwrNvUUAKpuHeaiHtTnILNtFOF3ZaDDcAjSSMs/PYkioyg2QRQJASrQqacUUUwaX5tUUey0qjBxmFUUhTWKtKLoTtTp/Q5pC0L7qnmiuGu8J7B9AHzqKR9kAwgUxe6wno5QrTGW9DuEe55LmpL0V2wVyEpYMacJC8FfOcH2rbRsZ3Uu6gIEAHuP+b8/WpGko6VrpuUP4AmsW2SQt6nFDrWFvx2GKLaqJkiG2UhIzCm0skZxAUUUxwJhNt3WHqPxfTgwOhP5eBpWuowe/51EvU9a1tPQMBuLD7PhahRTUBTv+WX0cQz7/PT12KJsnxcPDTAtqwhoRTG0PQblCOPDbKBmGt5XHsR+SqynNSrRlD+HeYKVWFGk+lBOlQ1GUeyl+ijSINZTRSQWRcEoQwO1SKQEyXQaafmnKVGkUkybqVEkKaRhimKMzdLrB0heR3SSMO0xWmg9NY34wWdgKCL1VBBmYwqIIt0WxO1f+IxyPuOr5oniK1VBoikP3npaEBBFUZhNF4Au2CdEkRTuT5UNz0PfbpCVsN5chln5LLtE8dUTwR5KVcOKHdRfpuoaaXWRHmBrpu197iSra7ogzEZtpfVUazFRNA3g+fv928NPsaFSEhISXQdNYD1Nq76cVhTpNFCAtZ52gigqisKEgTRDjuOSsNOw0iVJ7ox6n0xKYTa8BZEgkHqqctbTFBTFozHHMezz0yS7alqeOhdWb0kvAI+HhdlQiqLTR5FVTxvdVx7keJPFbrq8hT8n+FYh9VpPAcd+Ol0OWk8DiqIbZqNYdRLFCjsHy4Elimml5Z+mRDFBjWIuAVEkfQ3DiGKIzdID6QdIXkekKLYjzIaE7tgxk9BLb2b7RD76d/7kVaQoEqLIE1zSFsTtX/jXi/8H46uu2Cyhq/UmqFHM84qiyHranUTx2AStKDrHan7E6nG7QOoHe3Ma0xh4pmJg645D2LYz2NC1YlixgzprPXXeI59RmQkOrSgmsW55keoMUaR/O61LPU19YkZ+jz/8S/++8jgbKiUhIdF1ECuK6YwPg4WMV8ERVBQ7SxQB9nM2Y7ct5qKvy2lY6ZKUC0S9D08qGlYUk/ZRbEGYzbKY4xj2+envuVyzUHUXdAsZ8femqYqXcRBuPfUTznOo+kRRzQCK0vC+8iAKLil5YRRFLVpRXNSXjCjSeQ5TZcMr4cnqqneeBFNPneOhijofRIHr8U6IYj4TbFXWDE5PophWjSIhbpmCrwrSRDHEZgmA7QdInmsZgMWpR+0Is4kK3SE4/13ASz8ATr7g33fyBX/yqlFEkRQcEtIYQ3Bv3MhK4xWw269fe1HsR2gszKY7ahSPTwYVRXr1eGxG7M1vJSzL9ga4Ilej+D9/9FJo/eFMpRY7qNMXVTJgF7M6c3/NtPxGuIkUxWDqqUJ3GErdeuofj9QVxaQhWBISEl0FEUFKS1HUNdWz8fElCSxR7Mx1LS1F8fylIYv3LtKw0iVRFKPeJ2A9bbBOPYwoihXFdGsU+XkXj7DPT3/PpAUEEP5ZAN9+GhZmYzFEsYasm3pqu9s1uq88yD4SFZQOweEXdILW06Q1iqzVliiKdM1jIPXUrVFU61UUq6yimPWIopZqWv7pSRTTSj0lk09F8VXFWYooEpvlVZ/w7xs4I9gPUKdOQF5VbEeYTVzozrzVwDnvAPZ9R/z43geAmRPuDdu3y9JhNhHYvGEIV53tEzveevq2N26M3j9AUKM4dxRFEVGkE+5GZwRKc4sxWzM9vl/M6Shk/aFi18HxkGcBD+w+GjuoL6AsHKRGsSenMRfvmmlTimIdNYp2yMA4l9pjJA3BkpCQ6CqIavPSqlEEfKcJH3JWqXW2RhFgyVczNYoXLAtZvEd6Vrq4xb249wmE2TTYkqQnrEaRTj1VLOfY0iJCCori5g1DuHbNUuFjUZ9fURTv89OLxWGfxTAtWO5k4tUT09h85+N4fP8Es82jB33CmUMNmhvKUjYVGKbV8L7yIPZY33rqLybzvUBXNKgo9hf8eeUkVaNIK+VhiqJu1ykK0FwGfo3i+GzNKxFKA6cpUaQUxbrCbLgBnyZAhKjQiiLg2CyXr/dv/+ptwX6AGvU6fC9Fk1LlWtUcPi50x6wCT3895jWoujRSpygKsxFAUxX89n86y7tNE0U72wutJ0GNIrUiBSBEUeSIYYcDQkgLiXspC+cvXhmFadkdVxSZpK6sHlp0z+O7zxyNHNQBdjWN2FCLWZ25+BqmRfU3SmA9dScpZtiQ1kLraeqKYtIQLAkJia5CK2sUAX8B8VSEotiycK0Y0ESRn3TXA9HC21kLelK10okUxWJWE6Z2Cvex1YoiXY4IOxhmk4KiqKkK7rh5HW6/fi02rpqH5QP5xJ+fHD8vnAbiz0IS0IfdJFvLBnYeGMPXnmCvcc+O+u+VU/wwm5KpYPtTR5raVxpkHkMyEOj8B94NsJgjhrd/P77HpmFaePawT4I/9+BenJhy5vC9UYqiW6OowqqvtKTCK4r+c8n7poHukFTajUSppwJFsTDIEkGaAJGUTZ4oAsCs39AVxYXBxxlFkZOePbLVwiCbgRXAZMTkc2AofnJao+rSCNlNqCgCTk8+ArpGURkYSkaQbdsJ2iEk9Ud/BUwfd+y/ZFDtIkWRDKB8Ouinv/M8nth/CpvW+3WZneilyPf+ySckiscny96gvuWffu6pj5cMDeDQWAmnZqooU1fBGaoQnr74VuusUSRk0goliq0Ls0nd6pXk9yghIdF1ENYoptQeA/BLEqYrBiqG6Y091S6wntKfsxkyJzpen920Fr9ydoI2WQkhIrKffPt5+Ai1YB39fPY6k29QUczpKhSFaYUHAKhZqifjaIH2GEpqooGuqdiycSW2xLiAgs9TgJrfKxAQE8WwBHSDu05P2n6yfQ5+mI0BHVt3HMKWjSsb3lcadOCO076CTj3198kwLfyv/3iFee6+Y06PzUf2jeCOm9cFnE6iOd2BUb91BU0OwxRFAM68mU+FDUOVD7Px54ojE9NY+do2x6E0cQQXLFTPT/aiQZyeimKjNYq8SiVSFGszQVVwZpR6DQFRTKQotlD9ShK6E5fgSlt4A4piPFE8OuE8R4OJlYrfABdTR+PTHkn4B524OrInGP7RRTWKcT2Mnjns2zuJzYgokJvvfBxXfu5hbL7z8dgVrkbB9v7REiuKxFaqa6o3kOZ0Ff9+y1Wex79EpdqRGsWerM4M1DXT9hvhNtgeg0ELraepW72ShmBJSEh0FUTuh2ZsmDQM08IE1Uvu+jt/7o3/TI1ig6SlWdCLZ83YbUVqHwl5Swsi1bWe9+A/X1ZrbC6hKApj2SRpmDWuj6JG1yh2QbYC+a6nYqynYQnoBqdRTcCfb9NhNjVoqaTcEvDp7WzqqX/Mt+86gp+8dBIihPVtjGsLRh+rIkcUK/Tx4DlAFLgwG6IoajCx4oe/78yBDz0BTB5GTwZJmsMLcXoSRcZ6WkeNIk8UaQJEh6mUuBquWZooCto26FFEMSQ5NE0kCd2Jm7wuWeP/HVAU4yfpR8dL0GDijsyXcJX2vP9AeSI+7TFp+EcXKYpxLST+44UT3t+nZqpME/udB8YwPFHGzgNjuG3bbtzy9V1MUXYaYHr/5JMrilef6y+EHJ1wBvjlgwUoiuKRTeKdrxp+YE1PVmMGasO0vEE8WXuMYOopg5QXWlraRzFpCJaEhERXoVV9FMn4/+Rrp7z7nj0y4Y3/5Zo/XqdeM50QtBKYpK48DKJjuHQgL9iycYjI6NL+5O8RsJ42cQ2gyQtprSDso0gWy1OoT2wWItVXpCiGJaDXwG47YdNEkVIUbS2VlFtvH6lk1tmqyaWe+t9hI30b454zQmVR9ATCbGhFsQ4HWcB66iwkbdIew9LhHyZ/nRic3kRRzbB9AWno2aACxauB9OQzrEUGEG891RKE2bQqyAYQ9jbEyivY0J24yevSS6h9LjuF15Z78UqoKG7SHsM7tR0BGwaA6LTHpOEfXUQU41pInJz2z4NTM9VYBVK0wtUMSGsMgITZ+APbWQvCF6beeJazmGJZNo65KvEy9yJPyCYpJKeVxWJOZ5LkaqYFs47UU3Lht14PimKS36OEhETXQURy0qipixv/9x3zy2k6pSiyYTZNWE+55/bl9dB+g41CrFomJ4r88xu9BhimxWTUkLYnvKKY0VS/j2IXjP+i4yciimEJ6IbNbjtu+8IM3UfRgJZKyi0Baz01GOsp3Ruzkb6Ncc8pu6q/YVp4ZO8I85hFc416FEXeeuqG2dyoPZr8NRLg9K5RzPVFe72zRad/GUEPl6wpsp4CQOkUux1RFNWMuCYyUlFMXufXFEhvw3UfED9OJq+773aI18Rhp1Zq/W85CsdP/t7f1qhwbT3iJ+nDEyX8oXtyh34lu+4S71/S8I+A9bRzp/+ywQKGJ8qhj68YLODkdBWGZWNstppohasZ7z4P1nrKhtn8p3MW4tylvXhoz3EATmz0SbcBNCkOPzld8VbryIpggUocs22//QbgKoqURatq2qjV00dR0B6DQYpE0TAt/PI1fzHo4X0jjiV4w1B6wRVxv0cJCYmug2hRKw1FMW78f+m4P2HsXI1ia6yn9Sh9yd8juH9JUy0BgfW0AaJIVGI6g4DU79O19ippj9FFiqKQKApcRzduXImdB4K5HQanKF589irAnablUIPutsfI5/OppNx6+0gRxVLVhKqKVfC4+ZlI5Yx7Tl9eD82mmLV0T7azjUrYLCYILvWUWE+XKaOirRvG6akokhrFsPpEAt6WGrCeUpPPKEWR1Cj2LBCzIK3DYTZJQSavH3kI+OQe53+S4EoTWbPCEt6YfbdtG0fHy/End1igTlz9JAn/CCiKnRtw41pI3Hj5GV5wwamZakMrXM2ACbPJstbTimFi9SJ/BZAmqKRWhh4wl7uKIrmQ2Laz3WyVDczhradmHTWKZJvwMJt0rKdkoL/rFwe8+05MVVpmAZaQkJg7UFukKMaN//RY2qnU02yLrKdp206BoD13Xk8mcXkFEOyj2IiiGKUSm3R7DE9RJDWKnZ+2i4h2j0BRDEtA54nin/3mRq9sJIcaCqpzHV0+vy/V1GB6H0s1LsyG+kyN9G2Me87aocHQ77xKaXalch1zOb49hhtmc9ROL/gJOF2JIq0oRoEPtIkMs6FqDwPWU4ooipBIUWyh9TQN0BZeo8wS3hg1Z6JUQ6lmxp/cYWmPScM/eAWxg4pikr5AdBR6XBP7NH38QDD1lFmJq1mM4jifauVRMZyLGT2xIftOrziWayZjb+3JagHraT3tMXRPUWxt6mm7LcASEhJzB61SFOPGf1pF7FQfRXpBrzlFkW9T0ApFkT1G9dhOATDuF1VpjBhHqcS0M0Yj7TE8RbHz03bR4m1BYA8mCeg3UsRq9cIibn3bBezr5XpgKM48OIca5uWdz6+kmC1gmBaeO+K3rvir+/fgR88f927T32kjfRvj2oJdfub80O+crlGcmZkJ/xCBJwbbYyzszeIe85rkr5EAnT/jOgFSo5gbiN6OJoqKCuS5fn5amPWUIoq27dcoFkOIEP063awoRoEnu/TniCG5pMcOObmFNYpAeNpj0vAPnizwVtQ2ggyg7710uXffGxYVmb5A8ylFccuGaPtFmj5+IGg9zVOTj1LVZIgkTRRJTPsRiigSEpvnVvNo62kxqzMDdc2062qP0a4axUaK3CUkJE4PiGsUm59mxakVS1zVTVXSIaaNgF7oa65GkbOeDqQ/9+EVwcX1EkXqmtSoghulEvPWUyb1tBusp4LPHJaMrmsq3nzeIu/2777pLFgKSyq37x5FTXHmY3mlCsVy031TmqMRJ9C2Xf5C7isnZvCVx16l9pNt71Jv30b6OWfM7wk83l/QQ79zuj1GqdS4otinG1g+WMA282o8jMuTv04MTr8aRdMAam5vk3oUxUwPkOFW9ZKE2VSnfdIUqihSk9jXi6JYh/WUpGNuM6/GW9Sn8E5tR3CjqLTHuPrJLuyjCDgD6EpqQPnCjZdi7ZC/GEEIWMWw8Otrl+HHL5wQqllhK1zNgFUUNeiaiqymompaKNdM0Auo8xhF0SGKRyOsp4BDNukwm0JWQ0b3B9+aaXm2kGTtMSIURUVNzWbcbguwhITE3IFoAtlM83mCzRuG8Mi+kdDxf9xtfJ7VVSgp9dirF3QYSDOfmV8YbEeN4tI6229kmDCzxq4tUTVtdHq3Zz0limI3hNkIznOR9ZSAVhu/9vMDqBx/BddSh/y2+1/EVXkdvXAURT8IMR2iGNe6Agiqwo30bSTPOXNBEVv+6efMY8WcHvqdVykrbqk0G3g8FFzqaZ9uYXFfDhZU/FH5t/FU/knngUwPZmvT04JXSITTT1GsJOihSEC3yMj0BBNSQ8NsKKI4QyWeinooApyiSBEs2/ZJZitTT9NAM4qi+8OxoOLW2sfxx7WP4lDv2vrSHqPqJwkC1tPOD7h0uilfTD+v6A+SE6Ua7rh5HWMrWtyXi1zhagYzXHsMwG8qXKJsoxlNQX/eP64+UYy2njqKIp16qnF9FOtsj6FGKIop/nbabQGWkJCYO2hV6ilRKz636WLvvoFCxhv/q67lv1NBNgCvyDQ+teQXBuu1hSYBb89txnraqKIYpRIzRFFxw2yI1aobFEWB1TaqxrNIkch9x6aY9hiGrcKAjpJFFMUaFJMoiuks5sc5gQAx+W0U5ywOttcr5vTQ75xWFMv11Chy1tOiZnjqeA8oLnHJ+7D3pPVC8hdmcfopirRUG6soUl92VqQoUhNQ+rWe/iZwfI9TO7foPP9+UWsMgFMUKYJl+s11u58oNqEoUiqMCQ33mtcg/4YP4W/ee1G6+9hliiLgBKEQLCiyx4nUKAKO/XRRX45prHzF6gWB1S7DtLB91xHc88tDODpewrLBAm7cuLLuRE66frA35xynQlbDZNlAqWp6ttBiTmcmJxW39cUR107cl9eZ5xOUayZmK3Tqqc4EDFQNC+5bJFqdjlQUU/zthKW4EaRtAZaQkJg7ELkf0rKC6pqK911+Bj77vb2YKhs4e1HRG/+rbj13p+oTAZY8NPOZeUWxFUSRJzp1W08p90ujxzxKJeatp2yYTeeJoqgcJFpRZB+j22NUXJJE/i8oVYBYT1NSFOOcQEBzAUw85hWzWNibY4SAvpyOqzcsEn7nNFGs1BVmM8ncLGomFruCQ69CvU4c14lB52fK7QZ9YEWtKmgw1tNikCgSFc00gG/f4t9vlIBDTzj/hiifcJj1NExRZFpMdHuNImefTaAoElJzt6Cui/TaSxVd1B6DgBDFwZ4MszJpmBYOjPoWhE9ufSZQp0gPQuQ5fPTy8EQZOw+M4ZF9I7jj5nWJB0NiPVUVXwkk/5cN0wuaKWZ15kLpKYruwLyCUthyTJ2jxSqKWZ25+NLffyJFMapGMcWC+DgLWNoWYAkJibmDVtUo0pjXk8VU2fDspgBQcdsqdCrxFOCIYhPW04AttAWpp8224KCf3+gxJyrx9qeOYOuOQxgeL8GwbIxMVQLW024Ls+FTY4Foosj3wTQpRbEMt8SGEEVaCUtpjhbXugJIxyJO45zFvcwcrZjThd/58sEC3phfCrhB6pVKPUSRVRTzqolDp5x5Yy/817GykijWh7oURZooFgCdVxRd8vbMN4F93xG/xuEn/b8TpZ5WxX93fZhNfYpiWD8ZAtr6mBq6UFEk/QcX9frHSHRsXh6Zxmcf3Mc8d3SaDT5KksiZ1G9Pjn8xq3s1L8RaUq6yaiOtKJZqJr7xxAGMuAR4eKLk9RgscGE2jKLIWU9LNV85TUJu/Qu3AstWoCpUIlKKimLYQL/lspXYvD7FPooSEhJzDqLffzOkSYTBngwOngImZimiaHSDoki1x2hiHOSJ9cLeFoTZBFJP66xRpK2nTShRfB3cf/vO8/jnn+5niKLTR7H7FcWk1lMAjPWUJ4o9FMFJa44W5wQCkmUh1INzl/Ti56/6Ld+Is0pU+/jSIy97RLFaD1HkrKfl0owX2NNHKYpbnx1HM+j8TLndKDdYo5jtATJ8jaI7AX3qrmTvnYQoEhXRNICnv+Hf//KPnKCWS2/uioEiAIYoVmPV0Lji4v0n64gIToou6qMIOP0jiaJI1ycmKbwGgNEZVlGM8+Hf8+RBwEYiWypRFIs5f4ggF4JSjbaeasyK6iP7RvBvP/d7DE6WDNy2bTce2TeCt16w2Ls/UKPIWU/poJskkw56kmJBgQqaKKabbttIkbuEhMTrH6KxKu3Fo4GCM56Nl2qwbRuKonhp052tUaRTTxubdBumhZ+9dJK5b9vOw3WXTsSBJ3f12FsN08L3nzvq3T54atZbDG12HzVBCYUTZkMrip2f//FEW1WiFynoRWINJt6t+UEvCzCJG7RHvRYRpGk8AODAz1KZ90Y5gQjSXtBZvYhtr3frN3bh/W9cJTxP8gVfhKpVopVPD7xzD4BV8+eEtKL45HANas9Aw80VTz+iyCiKdVpP+TAbolRMJOydFlajqPFBMAZw34eBvQ/498+MAPffCrz0kJPwqXXZV8eoomVODQ0qOnGk5shYc+mRolq93z2vjGvpjVImEHHvz5OyyZLh1ZbQq6ZJCq8Bp27RtGzv9eJ8+HuGJ3Hbtt3e7Shbqk8U/cGZWE9nqyYMpkbRf97hkO/t+3uOMWS4XDVRqtI1ihozUJdrNFGMn3TQ25hQoYNqfN/t9b0SEhKvCwgVxZSJ4jy3dt20bExVDPTnM17/2lymc4pitknraZjLiCw01lM6Efc+NNEDgB/vG8ENG1fGEj3RPs5WzdT2kZwrvPWUaY/RBUIBTxQLGS0ybZdYTzWY+P8Ld+At9hPeY3mlhs9nvoIRW9CubnY0lXkvcQL980/3429dZ9aivhyuesMC/PtTw+5nSu93apgWvrubPY+fOTyBZw6Lz5N8vn6iaJQmAwQuB99lQNcoTqMArdAfQkDi0Xmzc7tR8RtuRhJF03ACaQgOPQHs+x67DZmADqxI9t5J2mOYVcfKSpNEGnsfcNpAdBt462mMohhHaspG4zWKZDC/bdtu7Dww5hGi23/4Crthi6ynYe9/27bduOXru2C45PBESOJpksJrALBsYGzWJ+RxiZxlKgiHhqhRPLGe9lKKIlkVrBiWpyj25vTEk5NfUDYMXlHsyWrMoOmRPgAAIABJREFUxadUq09RpCcmNl+nKImihIREG9Cq1FMagz1UGrZrPyXW02ZskM3AMC28csK3wX32u3uxdcch7zqRBElKJ9LYz1u/8VSgjONPtz/LXJs7tY9EieWtp057DHffukJRZM/pQjZ6LqWpCnK6ik3aY3iL/QT4s8IGsFiZED3VQQrzXl1TsWmdP1d/6/mLsXaF344sTevp9l1H8ORrp4SPic6TQsFvk2bWkhHFh3a9FLgvSxNFSlGcQg+gNt5j7/QiiqYBvPoT//aP/sqRtS0zuN19H2ZPzNIp4Id/wW5HCNC6kEbwPJKE2RiVeCvrroRW13aCb48RoyjGkZpmfrRhgzntiwfQMqKY9GJCJ57SRDHu2NCgi6XjmjJHgW8UT1JPe6nWF6KGusWcnnhycmrGPydKNROzVbpXo85cfBjraYKVPnofAsmnLVSOJSQkJAjaoSgOFvzxjCwUetbTiDqxVoGQr8coy+hLI9OBhdE4xDlp+GtUI2iW6LV6H0mLBsvmwmwYRbHz03ZeNY0KsqG3uVF7FAACkXOJfiEpzHvpMpmq4bfgAtK1ntZ7njRCFB9+5tXAfTnFJ4p0jeK0XQAsoxp4QkJ0/oxrFzw757f9+07sdWTtez/kPE4QpejRIATv0pudhvAikP6KuYHwCStTo1iNt7JOHI7ft3YjSlEUEMU4UpNk4AlD2I/UsNvTRzHpIEErirT1tB7CRwfabN4whLedv1i4XT5G9aMbxVcNy7PEFrPBGkUavTkduqYmmgwtpq2nVC9GUt+gKIr3OuUmFEUzQBSloighIdF6iGsU051mDVJtk8ZnazBMyysF6ESYTVoqW5yTZjih0yYKzRK9Vu8jqVEMpp6qXVWjyC8OixaRefRkdSxTRmO3C0UK816aKFZMCzXT1zZFvSEbRb3niZbx50Z0nWEUZqeCATV0fSetKE6jALM0eTLwhIQ4fYhiPXbOpOE0RMlVNcc/fd2XgR7KBpzrA2pui4NiRB2pxrWWiLOyDnRhBD+vKDLtMYLW080bhnDtmqWhL9eMXSfsR2q0SVFMOkiEKYpxx4YGrShqqoKP/upq5vHFfTncfv1aXLhM4P+nQDeKpxNnWetpcLggNYxJJijvvHiZ9zetKNLJqmSwZttj1FejKK2nEhISnYDaDkWRsp6Ol2reoh7QmfYYaalscU6a5XU4bcLQLNFr9T6ScyXYR7G7ahT5c5rvkyhCT1bDUbvhPJVU5r18r2aDIopp/k7rPk+0+oniymKwM0AuxHp62bmrYM1ONMzSX79E8eSLwBfWAF99uyNZ7/q36O1pWTtpOA1tGdV0YO1NwOAZ/n2VKb9NxPgBYOfXgjZXIJh6GmdlXZ/Q6tpOaPWF2ZDi4otW+HWi61YO4swFjgRPWw/rRdiPtMqX/raIKCYdJGiSR7fHIMfm9uvXMvf35YP7e5JrkfHSCBuXfOXZC7Bl40rcFNMInm4UP11hLaEEolVDUqROJigDBbFqfu2apfiNS5Z7t8tVX1HsoQJziP2UJopJisz51FMG0noqISHRBrQj9ZQhirNVr4ci0BlFMS2VLc5JsyXmGpYEzRK9Vu+jqEZRI+0xukhRzOiNKIoa7jGvAQBhjWIsUpj36poK8nPkradpKop1nyeizgchMEwLW3ccQq00FXiMrlGcr/sW1s/dfGXka8bh9UsUqzPA5GEnhOb+W4Fju6O3p2XtpOE0/AT0mW8Cw7vE21om8MDHgzZXgFMUq9FW1gveA1zyvmT7104EFMXoMBvA+dGSFZ2FvVl865arsHK+SxRrjRPFsB9puxTFpIMErSgu7GPJNGnB8LtXn+XdN1UOriDRZBMA9h1lB49J9zmbNwzhHRcuEe4P3yh+ppqcKBLFkcSyLx/IY/0ZfoH4pUMDuP36tfjy+9czr1Wqmd53TNtbPUWxSiuKSaynbOopA6koSkhItAHt6aPIWk9pRbET7THSUtminDT8NapRNEv0Wr2PotRTVSE1iiTMpvPT9gx3nicpFSpkNWwzr8YP7MuFNYojdkS4ZIrzXrKoXTVY62mav9O6zxOeA4SADkosTfvhP5bt7LuuWNBg4to1S/G21W7do6JBy/aIXi4xOn/GtQtGTIEoLWsnDafhCVASy6oovYlfTSBW1sUX+PcPvdGxtt7wta6wHgSgKL6qaPJhNmKiaNs2Dow61txVC5xWJISMGJaNWsIieB5hP9J2EcWkgwQhiqoCLCiKj9HKecEf+ArqwjvKEcUXjnFEseSsMGmqgr949wXMY6vm93gkjkxwDNPC/W5cNADc/8wRL70uL7gYEPJHkk8rpoUFlAp678euxBY3dpy2p5RqlmdxZRVF93UMusg8fpiKVhQlUZSQkGg9RCFsqSuKBVpRrHVcUUxLZaOdNBtXzcPygTw2rpoXuEY1g2aJXqv3URf0UdRhOZZmq3usp4H2GAmIYjGrw4KKj1X+AN9e9efYYZ2LI/YC7LDOwYneC7BYmQw+qTAPeM+XUp33Evtp1bSYoKU0U0/rPk8oDqCY1dC0YLoeuBez3v3j8Fv53f7e8/Dl96+HUnXngrk+Z37eBLqsGV8HQcval97s9G0R1TRmi45aCQQJUFLL6q67gHUf8G8ztk2XYGk6YLsnS/8K4Hd+kOy1Owk975JEXlEUT9RHpiqeqrTKtZyyZMJsyA5AfqSb7nwcuw87qy7FnIa/evc64LvUhi0iipqq4O82Xxwo8P+9q1fjtmvP9wYJogbOL+ZCLzBEYaVx9uJeHHHtPLT11LZt7D3GDraTZd+KcHSCJZXvv+IMr2m8YVq495eH8XcP7cP4rP+c4fGy1yPqsjPnBfall6tRrFAEMKurzPeXpyYxpaqJWVc17KEURdGqXpLaAfp9bJl6KiEh0QGIhqr0axRpRbHq9VAEOkMUo5qZ16uyESfNliYSvKNA5gbbn3IWQIfHS1g+WMCWy1Zi8/pgI/R276OwjyLZJVKj2AWKIr94m8R6SuZ2JjQ8nP813F9dAwC4QXsUn5/+CmxbwGdKYw5BTJEcE0WxRoVAAen2UQTqPE+oxeysUsN02cBAT3DeQtcD98IXv07Z/ZivOGVHm9cucgYi0jM+rl98AkiiCARlbaLo7b7bIXUThx3Fcf1vAb/4R7+/Ik+ABlY4dtc48OlNquqQFstgCRYhnv0JrbCdhp4DKnBrFOnUU7Fatv/kjPf3WZyiCDh1bP35xib5jhfd/+EPDfbghsvOBL6rwHPEt4goAsAr1GcjWL2oyFyIiKJIB9nwECmKi/tymNeTwdhszVMUDdPCV3+6P2BPnSj5pO/QqVnmMbJtWKNjGt/fcwzFfISiqPs9FkmNY1+OPb66piKrqaiaFko1w7O40rYVUauNJJYQejImracSEhKdgKIo0FSFUQTSVCoAtg58vFRj3BedIIppkK92otVktBmIahR1xf1+ifW0KxTF+q2n9DYjUz7J8VpmhJ0mvLDSJDxF0bAY11oS51KrYCgZj4xlYeD9X/0FPnjFmdi8gf390PXARcU/hqPoxxvgOsHI3LtCKYpN4vQmivNXA2/6Lw5J5H98mu6cnPQJahrATz7v3/72LcDlv+cokKrmWFYPPRH/vqL0Ji3nEEWiKJYnASId9y8Pbt+NIC0yAqmn4on6gVGfTK1a6BJFTlFsBkeoH5UX0KJlfTLeQqLI1woC8Gy2hmnhvp2HMeISxSNjs9i641BgUACAgZ4M+vI6QwAXFLNY2JvD2GwNJ6erkUTv5JTzuK6pODQmJopR8eY0dh0IxjH7RJFYRk3vWBdzweObzzhEcWym5gnmohpFGvUqijLMRkJColPgiaKWslKhqQr68zomywbGZqsMUexE6inQ3eRrLkGUeqor7rnUTWE23HVaVJbCg3YOjUz6QkJsy4yU28HRNYr075Svu2wXDNPCH2x9Hne6t7Oo4bkjk56T646b13kkdtlgAcMTDkGkradjNkUGzfSJYuc17E6iuNghgklWaEgfxrHX/PuOPcv2YYwKoaEhSm8iZIp8yZOUjbUb22HwMCk19OSLwJ5v+Y+FKIqvjfonukhRbIYoVgyTCYuZIhZMmjS0cGVu37Gg3/7A6KxH6v50+7Pe/ZNlI7I58Rmc/XR+MYsFvc75cnK6gm07D4cSPdO2sXWnY1c4dIpNnyO21Lh4c4Lx2WCRNQmzoQffmQiiSBYCaBL/i1dHvTpIkXqYpD0Gaz2VNYoSEhKdgcZJI2lbTwHffjoxW+Osp50nERKNQ/OIon/O+Ipi99Yo9mTiF91pRZGem8W2zEh5/pv1FrX5MJvO0KHtu47gwedHULOd40P3QuT7kNL1wL0KbT2lyKBRASxLEsXUUM9KRZI+jMSy+hv/ABTmi7cNS28iZIrIxnS9Y7dbTwmJnj7u3LYMYOaE/3jICthrlD3zDFKjSBHF2SZaZBybYMOLZqombNtmVMT/9dgBXPm5h7H5zsc9ohIFEku8+c7HQ59Httm+y/n+FOozHTg101BzYt5+Ot9VFAFnsPtmTJ+qe550iSKnKE67imJcvDnBwt4g4Q8qipanVPLWUwDIu8eCtsSOzlQ9oiwiiklqB+jnmba0nkpISHQGPDFshfVynlu/NF6qoUpbTzOn95RuroNc6/j2GKDaOHSDoshfp+u1nk5RLbi8lhlh06+U28F5i9om2x4j7XTipCAL9aR9W06pMY/TfUjpMKYiXaMIjijWZuCVWEmiGIEkBb/1rFTEJZqSPoyaDqz/IPBHLzkppSuvAPqHnP+jUks9RdFVbehax6TtOjqFKBINAPvuZ24SMvXoCw6Z1FUFDz13DKZlM9bTchNE8QhHfkzLRrlmwaYUxVdOVTA8UcbOA2ORih7ZZxJLvPPAmPB59DbEfmnDV0YPjM7i7h0HI/db1Jx45Xw2XnygkMHYjK/uPXckaAmlQYjg4ZAaxbh4c4K3XRBsr9GbZWsUAZ/gF3PB85ye1PD4/p5jmJitBe5PMtHKqNJ6KiEh0XnwVtO0axQBYMBVFMdnqyhTzhtRjbfE3IHvnlGolge2ryYCXaEo8udZEutpIStWHbeZV+Mh63JxjWIL2sHRNYqGSVtPO/PbIfOzKpx5Cq0oAmwfUk1VcMdNF+Of1+7F5eo+AIAJBe9dQZU6GRVfTQRkjWIksr0AYtor1LNSEZdoyquTohrHKEQqil1uPY0j0c/cA1z2OwDEwSmGZXt+7CvP9m0IzVhPj4wFVbKpSg22qYLoc4bNDm5E0RPVWSRSAm1E1vpNlQ0MC/aLhqg5MZ98+i8/3Y9f7D/l3Y7gXgCA+b05VA0LRydZlXWq4pCyGzeuxM4DY5Gvce2apXjHRUtx53+8wtxPyKBoJVtkPZ0sBYkgjdGZoL01yQCuM+0xpKIoISHRGfDW01YoiqRFhmWzyddSUZzb4EPZVJjQFMuvTwS6JPWUUxQTpJ4WQ8ikBRW/X/0DbNIewxbtUVzSN43s/DOc+bkoP6RJsH0UO68okrpDoihmwc6RmD6kpgF9+2/jbS8+ALIersHG0PFHqG0kUUwOPQcgYlJe70pFXKJpsz5q0kfFUxT9XnZdH2YTS6L9x+MIFx0J3AxRHB4P9s2cqZiwq/CIosn3VQRwz5MHAduxAxwdL2HZYAE3blyJu2PsnVt3HEK0cdXBYDGLY1OV0MdFzYmXD+SZ2zRJTIKr3rAQw+OlgLWDKIqbNwzhR88fxw/2Hg/ub08Gn3rn+bh+w0rsPznNPJbTVc/XL0rb68sHhxe6JkAEkeKYSFFkwmwkUZSQkOgM+PGqFTWK86jr5Ai1AChrFOc26HPHuY6Z0NF9imIjfRSjtjGh4V7zGtxrXoMffehqvGFx8+QmDFn3N1Ll2mO04neaBGShvmpnACWoKDJ9SF33ng3wvikfRpkjirI9RjhERFFRgRWXARs+WP9KRVyiabM+ajKZJYoiIaWqDvQubu61W404Ej3on+hxwSk7KBJUasJ6KlLmpssGVEpFNATO6z3DTtqU9zquxTQfs1Irej8RDnN1gjz45sSGaeFfHz+Q6LXDsHZoIFCfCPg1ipqq4C/fc4FHFHsyGi5c3h+IN89zq4a9lGIomqAUBVaTQlZDtRQugeYzGpPiByRb6cswiqK0nkpISHQG7ahRHKB6KR6nEiQ70R5DIj2I2jwFFcVuIIrsOZ2EKPaEWE958POMtEGsp6Zle0FQuqpAabIpfaMgfUirLwUVxUAfUte9F7mnRhWoUGGKUlGMgJ4P3rfqKuDD32ns9S69GXjpIXEtXho+al5RJCpc3/KuWEGKRB0kOi44hU7WLDejKE6IracDagZwX1akKJZDfJzlWrS/c/lgATbgRReHYboS/plEzYm37zqCn758MvI1eczryWBBMYuXTzhhQZOlGv7jhROB7eiWG2NUbeDvvOksfPLt5wW255vq9lA1iKJYdt56apgW+vI6E2TD44z5PXj2yARzX5IaHz2yPYZUFCUkJNoDlVcUW2BpG6R6KR6netJ1qj2GRDqgr2MeUYTV9YpiM9ZTHvw8I23QiykkT6FTtlPA70M69t97gVkgqxhY2p/HJ99+brAPaZx7D3CspxY1x8o3ryi+fkcVXdCSYcmaxl+PJJrWE1BTD2hF0bb99hjdHmQDRLcF0bIMiY4LTqGbz6ddozhdNtBX9N9fpCg2ii2XrWSii+vFDRuH8OX3rw+sPidtXUHj679zBT67aa13+66fHxCmqVZNCzNuiww6rpr+Dmjwq4a0YihayaYVR1KbejiiRvPaNUtx5oKewP1JVuT1gGWHfgFJFCUkJNoDXlFM29JmmBZePO5by5541XfhSOvp3AZrPXX+dhTFLks9VdO1njayXaOgF1Nm3YX7TgXZEOiaiv5ep0VcFjVce9FSbNm4Mjj3ScIHWhBm07KjoyjKSkVRfqwoyl5FUfYoivIJ9/75iqL8UFGUl9z/51HP+ZSiKC8rivKCoijvoO7foCjKs+5jX1KSaMRaNti/rxmiCPgBNR95CPjkHuf/pH0Y4+ApihXgiX8Caq5V8OSLTqKqZSZqz9AREBK97oPBx4oLmeMTR6be7kb/Ao23x7BtO5B6CgAzVQODvT4R4RXFOHtpNmTV6do1S3HdJcth2XbD1p/9J2aEhChp6woaZy7sQX/BJ2kvjkyHbnu3S0STEMW8Xp/1tJeqUYyqTQV8opwRHL8k7TEykYqitJ5KSEi0B/w4nqQPbFKQBTe6Zp5eUJWC4tyG0HoaUBQ7/yVn9UbaYyS0nrZ4sYNObJ2pOq6qTiqKBErGmXdlYYSXXa1z3HmRM/65RBQBGAD+i23bFwC4AsAtiqJcCOBPATxs2/Y5AB52b8N97CYAawBcC+AfFcVbOrkTwEcBnOP+uzb+7W2gh+tlOHaA9Xp3E2jV4/t/4v89OwrcfyusrR/Cx7/+y8j2DB2FpotVRY6s031geFy7Zil+/eJl3u1GFEXDtPC/f/aaV+dGX7SnywYUijQYLlHMaApuv34tLlw2EPnaS/qDdua/vm4NvnjTpfjE3U/jT7c/G6ivS4qwGsekrSsIlg7k0ZPV0Z9PRo6+5fZ7HElAFFVVYYgwbS2NSz2NU0YJURbFuydRFDVVAdlMKooSEhKdAq+2pKkoxi24PfZSfWUKEt0FTUgU7e5LPeXO8SR1hUnIZE5XA9bttJEVWk87f0xVnRDFGmbD5r6uey+6RpEnil1sPbVt+6ht27vcv6cA7AWwAsB1AL7mbvY1AO91/74OwN22bVds294P4GUAlyuKsgxAv23bP7dt2wbwb9RzwjH2GjB1lL3vsb8H7v2Q0yC+2yCyylJQ9z2A4gv3Ch8La9TedhQXBO/jPpemKvj0dRd6t/MZFRtXzcPt16/Fl9+/nlGp6u2jSFZb//o7z3v30WrrRKnGkAYyEBuWjXdfvAxnLypGvv6SgSBRfMPiXnz76eHIi3cSiNJOgXgFlsfqhc5nGCgkI4rH3JpKRlHsFdT3uqBtIayiKLKe+tvGKaOEKItW9vh6iDCQwV4SRQkJiU6Bn+imGWYTt+D2w+eDydUScweiNk8BRbELrKeBGsUEamEy1bH1n43e9xm333WmQ4mnNFRXUcwpBkqVkBwH1733ozM/6d1Vyy8ArvqEv41ZmZthNoqinAlgHYAnACyxbfso4JBJRVFIpOcKAL+gnnbYva/m/s3fL3qfj8JRHrFhmQqgN7jR3geA3Xcn72/YQhimhe27juCeXx7Cb584hXfFbL9FexT3mtcIH9u645Cw/19b0RMkiraWxb07DjHtJjau8tzG+NivvgGfeNs53m16ZSpMUaSPG93CwrLtSMK26+C4kyLrouYmoNo28P/+n534ScRq7LVrluLAqZnA/c8cmsCPBG0l6gWfdkpAErFEn+uqsxfgZ6+MMved5RLFnqwGTVVibcnziw6JSmI9BZxC83E3latIEUGh9TTnk1XSKygMhCiLSGHSiVZGVVCFtJ5KSEh0Dq2sUYxbcBuJaL0k0f2glTrWeko5lboizIZLPU2kKMbTjVYH2QCsokgcYN2kKAJArRYRiqjp2N13Nd6GLwAAymf/OjLnvxv42Redx40yUJ5jRFFRlF4A2wD8oW3bkxHlhaIHwtqFCGe/tm1/BcBXAGDjci18hrzrro4TRb7x/GZdif02liujoY8lbc/QUvQsDNy1f8wQtpsgOG8pS+ZpxaokSBrljxv9moMxKtpzRyaAM/1t6BrFKJJ4w8YhfPo9a7D20z8AAJyzuBcvuXV/X/zRizD5BoV1QpR2SkASsbY/dQRbdxzC8HgJywcL2HLZSrz30hW45NM/YAg1IYqKoqA/rzNppiK88WyH3J+YdiYYfTk9spicJvK0tVScesrWptLfOw9ClEXW0yQ1igAZ7M0gUVQlUZSQkGgPgjWK6RHFuAW3ZQLXi8TcgSiUTUU3tsfwr9OKEp/vADjbKAoC/ZyZbdqgKIrmKt1Qo0i772qV6AUfo+zP97VsnnXuGdXUaxRbShQVRcnAIYlft217u3v3cUVRlrlq4jIAI+79hwHQssoQgGH3/iHB/Y1jIqLnX5vA1xpUED+ZHbYF1k4XYdbFtiJbdGoSTf8kHylFk6hzl7AncZ76EYsKeqNqNMYj2i4AwFS5xpAGQ9AeQ4Qd+09h052Pe81ZT81QLTwS1CSeuaAHC3tzODI2i5xLtCqGhRUu4QtEIHPQNRVbNq4MKMaGaWFxfw4HRv0eiUfGSjAtG5qqYKCQiSWKFy136jKJohilJgIsUYy3nvqPRymjNFEWDdhJwyAy0noqISHRYfBjeZpqRdyC26YN4gVHibkBpkbRVgGlOxVF+jpdyGiJehAqioKejIYZal43v5hl5lPtUBRFc5VOp54CYPI8zGp0mzVacdSyeTYLxChTRFEBMtElVUnQMqLoJpN+FcBe27a/QD10P4APAfhb9/9vU/d/Q1GULwBYDie05knbtk1FUaYURbkCjnX1gwD+oamdG+j8YMrXGlSpr8K2nVUaHltDbKdAuHWxrVAUJ+V00q+XrNrhp5gCYNUC9iTWNRVZTUXVtIR9FBtpF0GQ0zUcnKjiDPe2mbBE97VRtln9KDWwJcHvv/kNqduCibJ6gNu3//34azg6UcYdN69Dv0BhXdSbxWBP1lNEZyoGbNvGiNuLa2EMUSxQK4fFOohilDJKE2WR9TSpdYsoj5bNE0WpKEpISLQHAaKYoqIYteAGOOFkBV3D5g3Ri48S3QmagJmhimLnSQ3t/KmH3BWyOkMUF/fl2k4URa6l7lAU/QXtWjVaUTQrvqKYyRVYRdGkFMVcXyopua1UFK8C8FsAnlUU5Wn3vj+DQxC3KoryEQAHAdwAALZt71EUZSuA5+Ekpt5i214F78cA/CuAAoAH3X+Ng2oA3ynwtQZVSlEUkUT7/PdgunoD8HywcXqUdbHt6JnPEMUopVRTFeHFrJDVUC1ZmK0GQ4caaRdBYFk1jA6/ijPc383tmX/C18x3YJt5dVCFSgmt+m6ilFUSbiRKPv2P296MF45N4Tf/8XEAwFTZwHTFQNm1+cYpirQtlU09DQ7w9ONAuDJKQ0gUE1tPne3MQI2iVBQlJCTaA54YpknY6AW3e548iF0Hx5k6nKcPjePpQ+N4ZN8I7rh5XVfUXkkkhya0ntpce4zOKoqGaeG7u/2gyKmy4bRtS7A4UcxpOEl161rSn8e+Y75NstU9FIEw62kX/E5oRTGqRhGs4qhmeOtphSWKKaBlRNG27Z9CXF8IAG8Nec5nAHxGcP8vAVxU1w4UBuHwTQ4XvIdpAN8p8LUGQuVNUYGhy4H1vwXlkvfh7yomHvz0D72Hz1vSi4+8aXWsdbGt4OoUaxGnmA0IB5hCRsNEqYajE2VsvvNxJrBm6UB0jUZPVhP2X9Rg4u+sL2Gd9op330XqAXxe/Qreoj6FW2sfD/RVbBS6quDSlYOJbKWNIk5Z3brjUKCdxzK3dUYfRSCnyjUmyGZxBFE0TItZ/fvqY68ip6nYvGEooCjqXCuNpBDVIyZWFFVpPZWQkOgsWqkoAv6CG2xg58Fx4TZksbDjAXcSdSEjCLPpphpFUUZE1bRw27bdiRYneMWQn28kabPRLEREsRtST2lF0YohilaNEkx0zno68jww+pLz9+wpJ5Pl0pub2rUuoNEtwrwzgeu+DKy8Augfcv6/7svADV/r+IoMEGx7UBUpb4vOBz7ykBO8o2oYn2WJ75+/60Js2biye0giEEg+rUYQRdOyhX0gSWH0UTekhu4ZWTWiW2bMVk2vn15GU7Bx1TzML2axSXsM79R2BAqpbRt4p7YDm7TH6viQ0bhweT/u+9iVLf1ukrSb6C+wx3612/6jP+/fP10xEiWekgvE3qP+6t9ro7Pe98dPhnrzeqK6BR5i62l9NYq2TD2VkJDoEFoZZkMjyWKhxNyCJmiPocLsGkUxiZMpCnz7C34x+7S2nlLx+AljAAAgAElEQVRk72/xRdhfWAN89e0O0eP6vzNEUs+xiuKhJxz7KQAYJeD+W4F7PxTdezEGr1+iCMUhWB95CPjkHoZwdQP4xvNCQlWYz9wkyZQEohq+jqPIKoqLBuObffIDTFTT+ueGJ3HRiujXJB0h3nbBEtz3sSuxpD+PG7VHAQRtveT2FvdxAFjc25wC1Y5VsWUx4UXLBwsB6+nqhU7CbC9FFKfKBnNeLeoVE8W4C8RPX2ZTY4sJorBFEK1GJp1o+dZTqShKSEh0BvSimaYqDS2YJUHS3rQScwf0udONimKzixN0OYquKl57LoJ2t8cgSNqruWUwDeC43/97jXoQyuRhh/S5RI/p/25QXEDnrKci7H0Ai4pKeBpmDF7HRLG7oakK/vuWtZ76JVQUe1iieJIjilGEqt0wTAtbdxzCN59jew1ecc4yXLF6fsizfNADzGQ5Oqkzp6mJWiYQW0NfTseyiNYigN96ZGheAT/5k7fEvnYU2jHY8Yo0jy2XrQyE2RBFsZDRPPI1yVlPwxTFehs99+YaI4pZwfeadBDXvdRTWaMoISHRGWgcUWwVkiwWSswtsDWKzt+q3T2pp80uThS49lr8PKFjNYqddOWZBnDfh4EXvxe+Den/7sIyKEVRyzH9wcOwoKAE+9clhCSKHcRro7Oe+iVUFGOIYpqKIiF6m+98HFd+7mFsvvNxbN1xCJWaKbyfbuRObIm3bduNPePs53j0lYnIvjkE9ABTM6KfcHSiHNtIHgAWu7aG3ryOoxGtRQC/9chFyweQ01VPaQxT2M5d4vd/XNSbxWc3+SW07SCKvCJNgwToBImis8+KoqDPVRWnygbTpDmMKMZdIE5MsZ56uodiPRCRwqSTLVJnYAcURWk9lZCQaA/o8aqVE9Aki4UScwt0mUV4H8XOTdubXZygrae9OR093DyhUzWKHQ2zeeabDhGMw667/L8ZRTHnWuOix5qMhoZXzCVR7CDoeq+KLZjMctbTk1NsW4YkPfySgCZ6fE3g1bf/WHg/XVdI2xJHbdYW+tJoDXuGJ2P3gR5g4laVlg7kkYAnev73Yk7HPW5rEVGNIsC2HrFsv93D2YuL+Jv3rvEem9eTwe3Xr8U3fvcK7743nbMI77p4uXc7SfPZZkHS726/fi02rpqH5QN5bFw1D7dfvxZffv962LaN54cnmOfsHZ70CDZZyRseLzFq7i9eGRWS8LgLxLIB9vFeQeJqEogG7CTqsWFaODXr/D6k9VRCQqJToPu+tlJRTLJYKDG34FiVnb/JdUyxra6pUWx2caKHUhB7c3qgRKUtfRQbnGO0DE/dFb8N4PV/Ny0bmkVxAd2t84zJcqiZqK+vGwVJFDuIvUd9AiW2nrIqWMB6mpKiGFV/dnxK3M+FriukbYljYON4K9AxXRGkz3KgB5gzF/REbvtrFy6JfT0AWNLvqGO9OR3bzKvxoHmZsEbxQfMybDOvBuB8rlu+vgs53RmwKoaFd160zNv+bRcswZaNKxnLRKlmMupuO+wTgJ9+d9/HrsTjn3qrF6Bj2zZu/cZT+OaTrF30b7+/zyP4fe7+j0xVmL6Qf/PdvYFwISD+ArGJm5T0NqgoiqyncZMtstDx6gnH9sxbTw2llV2AJCQkJHzobVIU4xYLuyrkTiIxyDlj0mE2XVKj2OziRA9FBHvzeiDchr/dCoitpx2kQhPRAUAe3P7vs1UDOZrzkfrETFHwJB+jJftk5AYRkESxAyA2T1rJ0bICy1+brKeNNrEn+0/bEnlFsUoppWcvFJ/I/ABz7pLw3i/XrlmKS4YGE+3f4j5npaUvr8OCiltrH8cf1z6KHda5GNMXY4d1Lv649lHcUvsE01Lh+3uOwXalxnLNQqkaJIG0PZUniu2wT0QhSTJZlBotSi+Lu0DcsIG9QDQcZiMYsOMGcf7z8kTx/ucaHh8lJCQk6oKq0DWKrZ1ihS0WSpI4d0G+O896yiuKHbSeNrs4wVtP+V7L+Y71Uezg72VgRbLt3P7vs1UTOVA5HkRRLETMiy94D0Zm7OigjgjIpfY2Q9SHBgAmqioCDmLeehogiulYTxttYk/qCumekGM2S/Lo2svff/PZMG2HYA6Pl7B8sBDoNWiYFo4I9iejKfjMb16MzeuH8MAzw979N122Ek/sP4X9Jx01SVX81FNaUQQAExruNa/BveY1OHugiFem2eAdGiQoqFIzUaLVQpcEKoqCQsbp2ThbZbfpNFFMkkxG90MM24buwUU3ehZ9f/z1gU5WrQeZBgZx/vPyfRTvfeo4Nv3KBQ3tj4SEhEQ9aJeiKPH6hLMwasG0XespX6PY4eR+sjjRSI/OgPWUD7Npw9xJlIOQ6aSiuO63nHTTKFD932erJnIK5dIj/Rc1gdi0dC3wxt9znnvT1xvexdOSKBqmhe27juDuHQfx6olp1CwbGVXF6kW9uOmylYEG8GkiTO2p2qIwG956ytUopqQo0kSvHkyUa7jycw8zk/sx9DLb0Jba85b246IVA6EDDCHRj78SXPgwLRub1q2Apio4Su3rdZeuwM1vPAO/ccfPAPgkMaurGHDDXPjBCAAmS9HJqqROr1wzhYoiAI8oBrbpMFFMkkwW149SlF4Wd4HIaiqqrmW10dRTUa1A3GSL/7y8onhovGFrvoSEhERdoHvhSWVPol5onPVUsU02XKGD1tNmYJgW9lHlVr/YP4qHubT0TrXH6KiieOnNwEsPhQTaKMBvfAm49P3eAsFMxRArino++PQPfxfIx7eoi8NpZz2lg1t2HRzHeMnATMXEeKmGXQeDQS1pI0ztSdQeY6o17THi6s/CMFMxMTxRxoHRWe8+ExrGbd9iaij+53r64HhkWmmUZdKy/WN3bMInBssG8jhTYGld0p/z+lf1CUjLinnRdZBkMCkbllBRBHzSWKqajLrbaaKYJJmsNxcdNtNItHqOGoBF5DwJ+NW+JH3I+M9LK4qGrWLpvF7+KRISEhItgUaNV13RyFtiToEsllqhYTZzb9pO5t3//rTvBhudruK/fW8vs10h2/rPlhWG2XTwmKoacP2/Atd9GacWrMcRewEmbXdOky0C6z/IqMilmimuUdQ5S2KuPxWSCJxmRNEwLXxq+7OhZIRAVKOVFsLUHmF7jMI8789yzcQUFwrTrKLo9T588kBMsG59oOsUK7Z/gv/Ft5+LJOFxlsl73HAWWlFcOpBHfz6DhVwbC1KfCARtkPOLWdx8+RmR70USU+MURQCeqkjQjtTTKCRJJrtoRfQA0ki0eo763I0rikGiGAf+89JEsQZdxsRLSEi0De1qjyHx+gSvKAbbY8w9RTFKBKDRjrKdXLf1UQQATQfWfQDP/No9uKryD3jCcktlqtNOn0UKoYoibz3tT1j7mACnDVEkKxr37jycaHs6aCZNhKk9FU5RNKFi63NTngLH1ycCjbfHMEwL33ziIDZ+5ke4bdtuPHVoAgm6TSSCBhMa/P36fe3buEF71BnsEE3C4yyTR10l8dikQxTn9WS8gWU1pyqS+kQgqG4NzSvEhrMMzXO+p0oCRbFc664axbjPdt0ly4WDJb1NI9HqJCkWaJwo8gN2JsEAzn9ey/afY2sZGRMvISHRNrA1iqfNFEsiJZBzhqm1tyhi0OEaxUaQNDCxc9bT7vidkjnlFCjHW4VtL1eqmsgpIuspRxSThuQkQHccnTYg6YoGwTOHxwON5dNAmNrDK4rjdhG3bfcVOL4+EWhMUSSE+VPfehbjs9F1evVCg4k7Ml/CmarvO1+tHsPnM1/BlzNfhAZnf8NIeJxlcl7RkdaJoriU6t135kLWSsooihxpWTmvJza9iwxYpmVjquwfJ5Gi2Kn2GGGI+mxfvOlSfOLup/H9PccDzxvsyeBvN13ccLR6GtZTfhBPsh/8581TiauFfEHWCUlISLQN9Hgjxx6JesEris4Naq42BxXFpIGJ7Zg7iYhiR/soUiCpsFM2NRfmiOJMIPU0x/5PkKKieNqE2dTbAqJm2rht2248sm8Ed9y8LrUVh80bhvDIvpEAaaXbSAB+eihR4Ob3BJuGJyGKJLjnnl8ewtHxEjK6ytQUpolN2mN4p7YDtg2mX6FtA+/UdmCT9RjuNa8RBqUADoneeWAs9PUvP2s+qoblqavLBnwyeNZCtg6NWEcBpz0GDaIWRoWz0KrgGEWohTWKNSf51Huu3vmBPOyzbd1xKHTBZHy2BlVVGp7c0ANwWtbTpLUDzOd9YCWw07lf0YK/GwkJCYlWgbGedskEVGLuwO+jSJ07JiUUzEFFMWlg4h9tfQYf/JUzWxooKapR7Bblv0ekKJZ5RdFAP00UtdYTxe44Om1Aoy0g0q5XJOrHFav9oJpCVg0oinR66NYdh4TW00pMeww6uGfngbFA8EwUGvFs36g9CgDCpvYAsMV9PCwoJcoyCQDnL+3HyFTZCwBbShHFVfPZ17xnx0FPERZZT+NA1xmOUa0k6BUv8qO2bWCiJFYduw1JWmc0AsO0MEPV0H76gT0NKfL8edfQxYLuM6VFh/ZISEhIpAldKooSTUDnwmwAsERxDiqKSQMTnxuebHmgpK6pgXZe3bKgU3DdUFN2uPWUVhRtRXPqG4FgjaK0ntaPOFtjFNKuV9Q1lVG8BvKZQOrpONWPcHi8JCaKMS0O6rXb0lhQzOJN5yz0bifJZ1mmRPfzXO4+HhYuIrJMnrfEJ8xjs1V84xcHvduP7hvB1h2HUKmZ+PoTB5nXem10Frdt242P/Z+deOjZo8xj+0/OxBIYut5ubJYiipSiyKiOFJnsdI1iFJK0zqgXZEHi0Jj/3H3Hphoa8HlbSENF5gxRlIqihIRE+6BR6kTHQzIk5hzI+RNqPe0S9asexIkAPFoZKAkE5xldYz3NxCuKs1SNokWTQ2k9bR5xtsYoJJk88xbPZYMF3LgxvCcjrb4sHyzg2Um2+fspiihmdRVf+/mBwGuUYxTFeu22NFbO78EiKkn0guUD2H14IvI5R+0FWBFBFoftBbFBKbxl8qXjU/i1//ETAMC2nYfxGqWIDk+Ucdu23fi3n7+G54YnRS+HHzx/HD/gevX8y89ew/B4OdJSzCiKs9E1igCYBvadTj2NQpwFpJG2GFELEmTAT9qcl7eaNmT5pldcJVGUkJBoI+ghSyqKEvVCF9Yozm1FkYgA2586gq07DmF4vISJcg0zlXCxY+uOQ4nnDfUiq6nM/LlbrKeFBDWKs3TqqR5BFAfSC/HrjqPTBmzeMIRV88W98wYLjfWUI+0lNv3jz3DR//cQY/HceSC6J+NU2SGKhYyGmy47AzVoTFojbT19bXQWJ6aCimIppkaxUbst4ASbjLrkR1UQ204CAO4xrwHA9oalbz+cf0fdQSkDPf5381qIbTaMJEYhbsUqTC3syfhrKz0UaTwVojp2G5K0zqgXadpZeQtI84qitJ5KSEi0D6yieNpMsSRSApkfWXYIUZyDNYqALwLc97Er8fin3oqBfPS1uRF3U1JkuRyJblEUc7pji41UFGtUmI3uOxOZvwGgf3lq+3XajGKaqni1aqoCLOvPeUmQf/rO8yOfK5o80/V/uw6Oh7aqCCMkM1WHKBZzuivLL2PqFMft+Cbh47PBJFQa9dptB3syXnLl+GzNU8nm9WRxw8aVsdaBbebVeNC8TFij+KB5Gb6Dq+teYR0stE4RiiIwbJgNpRZSDWELYYE3XVyjGNc6o5FWEmnaWflC84ZqB6T1VEJCokOQNYoSzUCsKFK99OagoihC3Py0EXdTUvDtwbqlPYaiKOjJ6phkahRZJ99sxUBWpCjSc538IJBlW8Y1g9e99ZRYQr/x5EE8f9Rh5qsX9uKh/+yTFtOy8egLJ4T2ubDJcz31fyIJfdpVFPvyuifL1z6T8/rlqMUFWJXpiQyfmalGK4pJ7La9OR3nLe3DjZetxOb1Q/jVz/8Yh8dKGJutetL8/GJWaB1YNpDH6sW9ePXEDI66VoJbKx/HJusxbNEexXJlFMP2Amw1r8E282qsXx5PfnlkdRXFrBb7WRtBFIGhB5LxkNTTPK0ozviKbzeknoZB9D0uHyxgi/v9NzKxSdPOGgyzaWAAVyVRlJCQ6AxUpo+iJIoS9SE+zKY7SE2ziJufNuJuSopUshBahEJWw1Q1pkbRJYoKoyhSpDFF2ynwOieKRPXjCd3LJ6Zxy9d3eTVqZPL8J9t3Y9tOR/07e2ERv3fN2aGT53rq/0SEZNr1ZpM2ArqmwsrmgPI0AODic1bj31+OrkGMC2QJa8XB7oeBRb05bFq3ApqqYF5PFofHSpgo1TDt1lHOd/sXRrWTABxCfNu23bjXvAb3ujZUGo3+8Ad7spippm9DiCIwYYoiTRR7GHvq3FAUgfjvsV6kOeBrqgJF8e3KDVlCpPVUQkKiQ5CKokQz0IVhNnPfesojan7aqLspKfh5RdI2XO1AT1aLrlGkwmzUjEsOTQM4/ry/0cRhYNddwKU3p3K+dM/RaQGShGwQ6JqKX79omXf7w1ediS0bV8K2bWzdcQib73wcV37uYWy+83FPiUkKESGZrjhfdDHnf4kKtSJw3Cgmso7afEEgBUKArzl3UeRr0Mdi0K0JHJ3xFcUFvclUmVbYGgFgXjF+sn/Riv66XzeKwNBEkfDxrKYyFgWaEE5T4US8reH1jjS/d0VRmEG7+fYYUlGUkJBoH2QfRYlmoAmtp1Tq6etEURSl3JNysHqzLOpFQFHsot9pIaMxNYpWaYLhIE8dGkMO7sKBnndI4n0fBvZ+23+R8jhw/63AvR9ibcsN4nWtKCYJ2aBVlUV9PlE7MVURKpIkqCYuAIcGT0gM0/JIWG/OfR3TgGb5q0Zvfe3vsea8D+LGA6tZCwKHimFFtmPQNTXQR1AEciwGe5yJNc0/FxRzIc9i0QpbIxBfp3jtmqX44k2X4tvPDDPve/3GIfx43wge2nNc+JwoAiNKLuXvEx33fEaFwhdpvs6R9veeURUyDDYYZkOnnkpFUUJCon3QGEXx9TGpl2gfyDXPBnXtex0qikD67qakCGQhdNHvtCerYQZ5mFCgwcbe/Ydx267dzDa5nN8eQ33mm8DeB8QvtvcBYPfdwLoPNLVPr2uiWG/IxkKqHcSJ6UqkIjlONViPgoiQ0JHAvTnNWxFQZ09696+svIyVu/8r7lvwJtww+lGYEA8OlVo0UQSAw2PhdY4E5FiICDCxniZBK374gz3BferP6zh3SR9DRkTve/36oYYIjKjOsCerR94GujvxtJVI83vP6Crg1qQ2NIBLRVFCQqJD0GSNokQTECqKFq0onp5zjDTRrX0UATKvVDBl92BQmUFlhi/rsZF3rafHZm0sf+qu6BfcdZckilGoN2SDtliemKo01YcQAIpZDSNTFWzbeZjppzhV8X/0vXkdiFgRWD/zGP7mrCvxZ/svAQCcvaiIBb05PLn/FACgbJgYQLRqcngs3iZLjsU8ASlLaj1tFURE8UvvW4drzlsc+9xGCYyIfPO1hyJSeLoSxTRBk0OZeiohITGXIGsUJZoBKb0ItZ6+jhTFToFvj9EtqaeAP/ckRLEP7Pw9A19oOjRhYnkuvM0bAKdesUl0z9FpAertGZfTNY+UnJiu1tWHMKMq2HDGIM5c6HuLZ6omdh0M9lOkFcViTgdiVgTeNPWg9/cXb1qHC5f59XjlmF6Ks1XD64cYBXIsiPWURj2KYiswT7BPqxfWn6BaD3JC6ylHFLPx20jUj6zW5Iq8Kq2nEhISnQFNDrtJqZCYG/D6KIamnso5RrPgraeZLlrQIf25SZ1in8I6Ar36RAATNQ0YWBH9gikkoL6uiWIjIRuLXPvpyalKXX0IF/f/3/bePN7uqrz3f69zTnIyjwQUAjgUC2hFIDjWXm3VW7WoraCCUu1wve1P7e2v1Q63t1dvtbXXtr/rrbNWxapFBVHBAVBEUSYDCYRACCEkkJkkJCc5Sc6w9/7+/nieJ+u799lnnk8+79frnL339/td67vmtZ71PGutdt580Rls3dfczPOGB3bz19feT7VWHN/IBmBhext0DDwjML8rm7+uXDq3ToiJtY79UdYmnrlsXtNnymnRTHs32YLi4gZz2FmtidOWjt8ZO9Cf6WmjRrGvQl6C4uiZ1VbezGYkpqelRl8aRSHEBNIqjaIYBc3PUZyZaxQni6l6jiKUBUUb4y6kUVDM8kPb7Dlw/hUDe3jBIPeHwNRJnXEgNtm44oVnHr925rJ5A+6qFOsU9x7u5k0XDl0S7+qtDmqqevU923nXV9fQUVrfOL+9bdAZgT3pJMAK0OK5s+qEmME0iuX1ib/7ojMH3WGqmfZuqJvZjBeNYTpz+fxx74CbbWbTaFba7BiMqX40xnSgbbQz8jI9FUJMEm11axRn9BBLjAMnyq6nk8mU3vXUx5CHClPszE/dtJbMTcuC4hmnLLMjMM65uLln51wM51026jDN6DWKYDMFZ52SzRTf/7pz+fWzT+n3+dj5tKda45XPfgq3bNw74DmEwbzZbUM6MuOGB3Zz0sI8eF3Q3mYzAtvu6vNsASTgmtrLAThtyVxSSnVaqxAUK9Ua167Zwdfv3saug8d46pK5vHnV6RztyVvjnrF8Pq8895QB1+s10yhO9hrFxuMxnnHS/HF/Z/MdTQdfo9hMwBTDY/THY8j0VAgxOZStIKRRFMMlhJZaIY3ieNHX9HTqjNsaTU8BFnCMDkyOiTMUASppNlVaaL3kStvddM2XbU3i4pWmSTzvsjEpLzNeUATTDgYrFswZ8NnyERlPHunh45efz5994z6uu2/ngO62HTg25CMzfr4p7266oL0Nnn05bLqxz4Y2CaidfTFX3vciIG84UxZGuiu1AY/xeNryXNhWDsFcs3GNYkrNtYwTyeKG4zGevmL8BcVmaxT7mJ420yjK9HTUlAXFER2EK42iEGKSKDdZ2vVUDJfQQvdreqo1iqNmKmsUYzf9w0Uery9v66KjYoLi7JJG8eZHOvjoV9fw8cvPp+38t416d9P+mDpi9DhSJyguHNiMsvEsxbbWFp66eGDhMhjqkRlPljaXWTCnzST+S66E13+CJ5ddwI5iOatrz2L1eR9kxys+RdVnlrKgWK9RHOgYj637s+npUATFxl1Pl8ydNamzopVqjTs376+7tv9wD9Va0Y+LsaHprqdD0iiqER8tZXPTkWkUJSgKISaeSrXGbZtyf/Wde+1s3/Hur8TMoflmNtr1dCyZysdjxLiyrFGcU+08/r1setpTzOKGB3Zz7dpBdj4dJSecoDiYGWXjWYoAew7lIzYGE7aamW42sqA9K3Lnx/fWNjj/bWx87TW8pPtjXNrzAe5e+lp2HMpCZby7rFHsqtSGdIxHa0viB/fvHrTDWjhnVt1eIJO5kU1oSv/ppo11169Zs71uF9nxoNlmNo0axFmtqY8gI0Fx9JS1iNr1VAgxHYj+6vO3bTl+bfehrj67ngsxEE03szlUsmi79p1mYlgbeH8K0T99NIpT0fS0yIJi+YiMsqDY7UfjfWP16I7yG4ypkzrjSAh8y+bPHtSUrVGjCLDnkH3Obm2hOkhjP7ethUsH2QTnl0prJhe211v/Nr6/vO7x1CWm2Wxv2MxmKMd4VGvFkDqs1pZUt8voZG5kM5CmdLxnUWa1JhpllEZBMaXEvCFoGcXwqBMUtZmNEGIaMJn9lZg5tLY2ERQPPpa/714H170brn47VCuI4dO4RnEqmZ7ObbJGcVE6cvx7eY1iCIpD2R9lNJwYgqILfCsWDC70lJ/ZFxrFw6ZRPHlRO6cubX7ERHDa0nn84xufO+CxHKeVjt2Y3ygoNrx/R+l4i9OW2Lvr1ij2Vod1jMdQOqzymsTJ3MhmME3peM6iNG4aBP2Ymg5h3aIYHrNaR7lroARFIcQEM5n9lZg5tDUzPW3GhuttAxMxbPqank4dUajZGsXyERnlcxS7sfHNqcOQAUbC1EmdcaIoiuMC32DrExufOa5R7DBB8SmL5vDmAXYMBTu4Po7l+Mglz+W8lYuP3zv7KQv5xFsv4Eh3NhlYMKdeUFw0t+34bMe+zm52dvTVKNYfj1EbNEyNDNZhlTWKk2l6OpimdLxnUYYiKDZem9M246vUuDMq09NqBbavzr/v/KTMdIQQ485k91diZtDabDOb/ljz5XEOzcykj0Zximw6VanW+MUWW+Nc1ij+Wus6rpn9AW5rfw//ve0/jl/vLmys/qaLhicDDJcZP6rtONZLb9XW5Z00BO3Ysvmzj5sc7j3cTWd3hSM9Nsg8ZdEc3njhygG1hXFwfVtrC29adTrf/OMXc4oLnxt3H+bST9/Ow3sOH3czf3a9oJhSOh7OvYe72e4axZZkgipAe8NmNm+8cCWvOOfkQeMWDNRhVaq1urMZf/zQE5O2GH8wTel4z6I0HsraTFvYuBNqo4ZRDJ+64zGGYxJSrcA174B1X8/XDmyRmY4QYtyZ7P5KzAxmNVuj2B8d28c5NDOTvrueTr4oFGucP/czW+N8qLRG8Q2tt7Oq5WFOS/t5Zsuu7IaWOrljvJj81BknNu/t5MUfvpnLP5fPJxyKRrG1JbHM1+XtPdxdt5HNyYva67SFAx1cDznj97hmsgDWPH6Qh3aboDhvdmufzVAq1Rotfu2RJzr5+SP7jofr2jU7qNaKPsdjtLYk/udvnTvktOmvw4rwRvgAdnVM3mL8oWhvx5OhnZuoNYpjTdn0dFjnG913VZ8jZo4jMx0hxDgy2f2VmBnE5Oigpqdg5+WJYTMVdz1tXON8qKRRLPrR07zrWQf6yB3jwYw9R/FoT5WdHV3s7MiC3vIhmFFWqrXjheah3Yf446/cc/zeKa7RC23hQAfXw8CL26Hv1v8hqIUWsTj+D3qrthnNjx96gj975VnH3YT2r7N76KZ1/XVYQ1mMP1icx5I3XriSHz/0RNMwTcQsylA0in1MTyUojpry7N6wGsC1g3kNKmUAACAASURBVJjhrPnyuJ0zJIQ4sZns/krMDJruetofF1wxzqGZmTSank6FNYqNa5zLu56mfoZBz+pcS59dF8eBGSsoNqN8pmAzQlDb5cJlrYCH9+TzS1YMc2OXwRa391TqNXSDCZZgAtv5Zy45/rurYgLiwWM9/TmpY6AOayiL8SdSUAzt7bVrd/CN1dvYefAYpy6Zy5suOp03XrBy3GdR+mgLh2B6Ko3i6BnxGsWOQXYVlJmOEGKcmOz+SswMYo1ijUHKyzkXw3mXTUCIZh59j8eY/LrZuMa5vEaxX47uG6fQ1HNCCYprHjsw4P3BBLWNJaFxKAy2uL3WoE8eynmIAN9fl22Uu3pN2Dx0LG+Ze8mFK9myt5PNezup1AraWlp45skLePMgHdZUXIw/VO3teFA28YWh7XoqjeLomV3e9XQ4M32LT4NDAwiDMtMRQowjk9lfiZnBcY1i0aTvS62w8iLTJJ53Wf2ZwWLIlAXFtpZE6k9lN4E8dcncOgvIbtooiv61iQAsOGX8A8YJJigeODqw1m0wQe32R4YnvTdmfCONh7oP5TxEoG7dZJieHjyaBcVXnHMKv3npecMJKjB4eE+0xfgj2fVUx2OMnraRahTPvwK23dX/fZnpCCGEmMK0NTtHMVj2DPiDGyc4RDOPOkFxCqxPBFvjfI8rs1qp8vFZHxtYSAQ49+LxDxgzeDObZgwm6AwmqMUxG0NlsMXtZyyvVy0P9TzE8jmMoVE8WNIoLpk3q4+boaDF+PU0CvJD2vVUx2OMmjrT0+E04s+73MxxmiEzHSGEEFOcmBwtmg3PZ8+f4NDMTNpLY4xhbZg3jpRPVPid1p/x6tbVg7gAznn9OIfKmBopNEFc9oIzBrw/mKC2cukQbIZLDHSUBsBZJy+o+z3U8xDf/Pwzjjcm3ZW+GsWRCopDPfrjRGEopqfSKI495R3IhqVRbGmFS66E138CTn8hLFppn6//BFz6JZnpCCGEmNIMeI7i7AV9r4lhMxU1iuUTFd7efuvQHM0enkwyUk4Y09P2thYuvXBgQays+m3G00+aT7VWDHlRenlx+xd/voUNpWMnABbOqRfoBto1LQiB7e+uf5DO7grdrlHsKGkUF88dmaCoxfj1DGUzGx2PMfbUaxSHOZfV2mY7m2p3UyGEENOMAXc9lUZxTKgXFKeOvizWOFd+cgiGsiVK25xxDxPMYEGx0QSwtSXxzXu288YL+xd4BhPUrr5nO4e7Knz88vOHXLgi41/zK0/lOe+vty2f316f/GVB7eu/eHzAzWjmzGqhszvvetpR2vV0ydzh7c7aLLxajD80IbCP6akExVFRqdbYsOvQ8d+f/ulmWlMasN4KIYQQM4Ho55ruetoujeJYUBYUZ03BcUXr0tOhc+fgD7YNfjb8WDBjBcXZDevLjvZUj59D2J+gF4LaX197P1ff03z3xJGeJ7igvY1TF8+p2yxm4Zy+yT9UQa3d49e4mc3stpY+JpNiZPQ5R7GZ6akExTEjjqcpT9Q8tv/ooPVWCCGEmAnMGmgzG2kUx4TZo7FamgCSb8w36K6nE6RRnHopNEYc6uptej0Evf5oa23h0X1HBvT7G6uHdoxFI89sWJM4fxTr2UIYPL6ZjQuKS+bOmhJb/c4E2ktCX3tbCy1NZp6GYp4qhsZAx9MMVm+FEEKI6Y7WKI4/U3GNYh3Pu5yNS182+K6nrROjUZyxguJADCbojdd5gr/UICgumDOytYTQV6MYaxRHupGN6EtZM9ufAKhdT8eOwY6nGekEjRBCCDEdaDtueiqN4ngxewruelqmUiT+uvXPeV/vO1ldexY7iuUcLBryvmUWTFDYp14KTQCDCXqD7X460vME+wiK7WOhUawXFEe6kY3oS/l4jHn9mJSWzVFntaYpacYwXRivCRohhBBiOtCqzWzGlUq1xvfv33X89+MHjvKN1duo1opJDFU9167ZwZrth7m6+jIu7fkAL+n+GDdVV9U/NEFmp3CCCoqDCXrjdZ7gM06qr+T/ctPDIy6gYfLYXanRW63R2V0BYPEoNrIR9ZTNSuf0o1EsC4panzg6xmuCRgghhJgODKxRlOnpaIh9ED5w/YPHrx3z/Uve9dU1VKq1SQxdppl11X4W1V+YoI1s4AQVFAcT9MbjPMFKtcbnbt1Sd23TE50jLqBlQXEszlAUfakzPe1PozhbguJYMV4TNEIIIcR0IKySqoUExbFmuuyD0My6al+xuP6CNIqjZ1E/6/+GIuiVD75cdeZSTl08h1VnLuUjlzyXT7z1ghFt03/tmh38eOMTTe+NpICWhZgnDuedVJfI9HTMKAt+jWsRg7KgqDMUR8d4TNAIIYQQ0wWZno4f02UfhGbWVXv7CIoTp1GcscdjnLF8Hn95yXNHfHD8WJ8nOJQCOpx3ldfP7TlUEhSlURwzysJ4M21hpVrjlg1Z+H/icBffWL1NZ/6NkPI5oiOtt0IIIcR0pFKt8ZOHbEyhzWzGnumyD8KbV53OPY8dqLu2DwmKY06CKXVw/FgX0PLRDbs7uo9/12Y2Y0OlWuPOzU8e/33/9o46IbDZmX9dvTWd+TdKxnqCRgghhJjqNI4pdDzG2PPUJXPrzjJvZKrsg/DGC1fy44eeqBtf9jU91RrFGcdYb9RRPgx+d0mjuHieNrMZLdFgf/Znjx6/dvBYb9160uli6y6EEEKIqU3jmKKpoNguQXE0TJd9EJotf1u58oz6hyZwjeKM1ShONZqpkssMt4CWTSH3dGiN4lgyFCFwrE2JhRBCCHFi0jimkOnp2NNMUxdMtX0Q+lhX1WrwwVYoqv6ATE9nHGNdQMvr5/Yc1hrFsWQoQuB0sXUXQgghxNSmcUwh09OxZ1rvg9DSAvNPgs499lsaxZnHWBbQSrXGw7sPH/9916N5Ld0SnaM4aoYiBE4XW3chhBBCTG0axxTa9XR8mNb7IMxfkQXF1okb60tQnEDGooA220TlWG/1+Pf57TqiYbQMRQh80xibEgshhBDixKRxeVKj6WmNFlomUIskpiDzV+TvOkdR9MdA6+cAfrhhzwSGZmYylAXPOvNPCCGEEGNB45iiUaOY2hdAmsKmkWL8WXBy/q5dT0V/DLZ+7tM/2Uy1VkxQaGYmQxECm+1KterMpXzkkufyibdeMLVt3YUQQggxZWgcU5y0sH75StL6xBObagWO7M2/N90Ea74MtWr/bsaIVBQzU6hYtWpVcffdd092MMacF3/45gHNIsGEGZ3jNzoq1dr0XPAshBBCiOnNwW3w0efk38vPgvfMvDGtGALVClzzDthwfd9751wMl1wJrQOvJEwp3VMUxaqRvF5rFKcZg62fg3yEw7RcrDtFmNYLnoUQQggxfWlp2G9CG9mcuNx3VXMhEez6uq/B+W8bt9dL5TTNGGz9XPCN1QObqAohhBBCiClIahQUZXp6wrL2ywPfXzPI/VEiQXGaMdD6uTI6x08IIYQQYhrSqFFsl6B4wtKxY5D728f19RIUpxmx4PnM5fMGfE7n+AkhhBBCTENSw/BcpqcnLotPG+T++O6yL0FxGtLW2sK7XvZLAz6jc/yEEEIIIaYhWqMogvOvGPj+BYPcHyUSFKcpOsdPCCGEEGIGojWKInje5ba7aTPOuRjOu2xcX69dT6cpYYKqIxyEEEIIIWYQ0iiKoKXVjsBY9zXbuKZju5mbXnCFCYmNZWWMkaA4jdERDkIIIYQQMwxpFEWZ1jY7AmMcj8HoD5meCiGEEEIIMVWQRlFMESQoCiGEEEIIMVXos+upNIpicpCgKIQQQgghxFQhpXphURpFMUlIUBRCCCGEEGIqUV6nKEFRTBISFIUQQgghhJhKlNcpti+cvHCIExoJikIIIYQQQkwlpFEUUwAJikIIIYQQQkwlWiQoislHgqIQQgghhBBTibrNbLTrqZgcJCgKIYQQQggxlZBGUUwBJCgKIYQQQggxFahWYM2XoetQvrbuaqhVJy9M4oRl2giKKaXfTCltTCk9klL6q8kOjxBCCCGEEGNGtQLXvAOuezfUevP1698DV7/d7gsxgUwLQTGl1Ap8Ang1cC5wWUrp3MkNlRBCCCGEEGPEfVfBhuub39twPaz72sSGR5zwTAtBEXg+8EhRFI8WRdEDfA14/SSHSQghhBBCiLFh7ZcHvr9mkPtCjDHTRVA8DdhW+r3drwkhhBBCCDH96dgxyP3tExMOIZzpIiimJteKPg+l9M6U0t0ppbv37t07AcESQgghhBBiDFg8iA5k8cqJCYcQznQRFLcDp5d+rwR2Nj5UFMVni6JYVRTFqhUrVkxY4IQQQgghhBgV518x8P0LBrkvxBgzXQTF1cBZKaWnp5RmA28BrpvkMAkhhBBCCDE2PO9yOOfi5vfOuRjOu2xiwyNOeNomOwBDoSiKSkrp3cCNQCvwhaIoHpjkYAkhhBBCCDE2tLTCJVfa7qZrvmxrEhevNE3ieZfZfSEmkFQUfZb6zQhWrVpV3H333ZMdDCGEEEIIIYSYFFJK9xRFsWokbqeL6akQQgghhBBCiAlCgqIQQgghhBBCiDokKAohhBBCCCGEqEOCohBCCCGEEEKIOiQoCiGEEEIIIYSoQ4KiEEIIIYQQQog6JCgKIYQQQgghhKhDgqIQQgghhBBCiDokKAohhBBCCCGEqEOCohBCCCGEEEKIOiQoCiGEEEIIIYSoQ4KiEEIIIYQQQog6JCgKIYQQQgghhKhDgqIQQgghhBBCiDokKAohhBBCCCGEqEOCohBCCCGEEEKIOiQoCiGEEEIIIYSoIxVFMdlhGBdSSnuBI8A+4KSGT5pcG8o9uZf7yXQ/lcMm93Iv99PX/VQOm9zLvdxPX/dTOWwnkvv5RVGsYCQURTFj/4C7m32O9J7cy/1kup/KYZN7uZf76et+KodN7uVe7qev+6kcthPN/Uj/ZHoqhBBCCCGEEKIOCYpCCCGEEEIIIeqY6YLiZ/v5HOk9uZf7yXQ/lcMm93Iv99PX/VQOm9zLvdxPX/dTOWwnmvsRMWM3sxFCCCGEEEIIMTJmukZRCCGEEEIIIcQwaZvsAIwHKaUvAL8FPAnsAp4KnAEcAA4C3wQuBn4Z2OzOTgfm+PdD2Hayhf8BVID1wHPJ6ZaALkzgrgHt/ln1zznuvlp6vtV/h5Be+LMt/o6K+9Piz1f9s/DPGtDj3+f67/Ab/134e8IdQC/1+d3i12Y1PPOYp1V7Kf5lvw8Cy0puuoCjwJJS/HZ4+kX6zAY6PW5P8c+jHv5Wj0+kffjb42GgdD3CEeGuNcQx0jDSOOIb18r+VEpu4l6v30sld23YMSuLqM+LltJnmXBTa7gX5aCtFN7U5Ln4HWlf/l6+V/NwR9mrlb63AMf8uQWl+JfLUAXLl/J7K34/0i3KSJSlKNdbsfw+ueR3KvlVNPwuXy/Hq9kzZff9+TMQkf4DUa4jY03Ux3L9Hm4cBvO/P7/L+RvPlOt4+dnI15Ym96J9GiwPhhK3kcR/qP5GPIdS9saDZu/qL08aGUo5Hch9f/eq5D6kGY3tTVyjyfVmcaHJtf7C1+xdzQg/qtTXyaGm0VAYr3IIfcNdpll9bfbZ2JfEvW5yXg70npEwVnVlqGV+MhhqWJqV7bH0t9Hv4daNsQhLmegXBvJnvPvJ8lhrrBlKu1l+pr/ny+nUrJw3GwNOJmWZpSyfdGDjtZ3AXuBhTOZZhOXFRUVRdPXn6VSK4FhyJfCb/v3PgXOAZ2AD/suAtwNP+P2XA2uA9xZFMQc4GxtkHwDejSX2PwKPAKcBV2GC0Fewjuz/ApuA3wb+HROIvoAVrk5MsHoX1uD/Z+BCTAi6Bfhn96sK/K77t9HD9g13u9vvdQLfB7Zggm47cBj4G/f79zyMr3b/f4QJyduBdR6n+zBh9+v+rj/ChMLd/r5dwKOeLoeBt/l7XwusBt4AzMcKWs39bcEK31YP71GsYfmWx/FuD3NBLpQbPH2fBG70ONSAPcAHPD0exgT267FC/ikPywPAS4HHgU94OP/J/T8EfNDT73ZM6F0BXOvv2gJs83f0uF+bgR96uL+JCbLXeNg3eZq3efo9iOX3z7Fy8KfATz2u/+jh3oiVsxuBz7i/Oz3s53jYPoPlfafHuQD+j6fJHX7thR7Gbo/jMawsPuJ5mICfebrd4Wn/GNawfRu4FRMSj3nc9/vnt4CPeB495GHe7u865r9XA9/xcP0Pj9NuD8djWJld4eHf6fHd5u5/z8O0wz/v9fzr9WtHSu+6DSuHVc+vTn+uGyuPMXjc6teO+DNf8XTt9GtVj1uPh3mnh6fm736f3+vAGs6/x8ptFfieu/n//N7/xcr3A359t7+ny8PagdWjwtPpqx6Xo1j5q/h7YwLlCHCdu7vKr2119ze731H2I349WLnb7Xnc6X9H/dluf+dWrK07DHyR3Ekc8jjgz/e6nyFEHPP02eDXuv3Zqv/t9mcqDfHvxcrRJvf3sL/niD9/xPPmSawO1TwfK+72CFYHozz+KTlvu7DyH+/Z1eC+C1iLldUPeng/5J8F1h5sw8pYD1Z/dmJte2cpPtEmV8jtQI+H+7Cn8QGsrcbvrXE3Bz1td7l/hzxchbuN8tfjaVT4ez7lcYl03IS1KVWs7uLx7PL4RTjDr85S3nV4+CKfv+L3jvq98mTk7e7mEPAf7v9jni74uwpPtxbgntK1HZ4vD7n7Hs+XiGuB1R2wtnoXVh+inO32sHzWn93lYTni4dzrfu/zdO0hT7IexsomWL3swfqsSItI2zvczQH3s8fjdhCrNxHG3WRheC3WtlexNvIg1kd1YXm63Z+938N/q7uP+v+vWPtfA36MleWYnPm5p9XB0r1N5Am2iN8WD8vGUt7VPC0i3g/751b/7PQ8isnvGjbO6PVwR/3q8HD+lT+zHsvHL5Lby6s8be73z7s8HF8H/sT9+wa5/TvqaRsTvB/3ezf57wfI7e//9vRY7+9/yMP2EFamLga+VMqLHvejguXjj8ll8hDWlz1Grm9Vv1dg/XoVGyN0eZp1+Xt3+zt2+7P7yZOnUVa2+fNHyBO5Ff++gVwPerB+PvIxykak3wFym9/t/l9Hbrd6yULXMc+LhJXfXeRy0OVhAviJuzlGbq/2ud83uV/3YeOJEAwew+pwFbjB434vuYztc3+2Yu3Pe8h9SQX4755PFff7sZLfP3A/Hvd727AxzxH3o8e/3+VuvkXuy6Lvu9nj9G2Pf9TLteS++FNYX3hHKT33u1+Rb5EXD/u9j3sedGJjuBrWvhRYOTwGfNnTfZeHE6xN7HW/Cv9+j9/b4/68i9z+3uX3Ovzz//HPJz2cndiYpgA+Ru7/Kx6eb7ufP/W4fx7LvyP+vio2Hq1i8ksF+JrnyVZPpxpWf2rYWD3qzx5//k73783AO7G+4Jexif3D2DhzFSbn/FFRFM8GXuZx75cZKSgWRXErlnmVoijWFMZurPKfDyzFBsJgg+lfwzINLMHmYgPy6HA3+r1ZWCHpwApCK1YwKIriBqxjBhM+o/HpBV4H7C2K4mYPVxU4D/gc+VDMEFxPxjrcb2HC4Ga/txGY53F4KbmwvxArQDvJncwR4AVkLdhej9MK/311KW7RgC7z+P0KeSBwGbkgz/LwzPI0a8UqdhtWSeaQBylzPW5t/s797v86f2YDpuX9Z0yr9VqskC/DGpRWrIOdhw3c5wGf9Hd0kCv23f7s68iDXYCF7g4P6/MxAXeJv2O7+/nfgKdjjcFc4F88v16ANSpPB55JHrCdDqzEBi8vxhqobqz8bPS4PeFh+ltMaH8AWI5V6mdg5eombMKghTzoXu6f12N5vsP9aSVrXyvYZMU1fv16smYZf67F4/wccue3yP2L9IgB7Jl+bbu7bXP/vkfu2F6GlevNZG3iRViZnV3KhyexcvPL/s5T3a9TsPIZWtZ5nmZRNrZ7XD7k/oWW69fJs5k/wsreXf7Mv5ae3e3vC0F6H9bB7CQLKBv9e2hcl3m8a+5Pjdwx3+zhm+9uH/e0SVhn1IqVxRBObiULCGWta9T/feQB9U/8c7E/c42HqcfDHHU1Bo0JKz9RV+MzNH63Y+WtFyuPMRCdj3Uw+GcrVgcTudNbAbzV70VbEp/LsA6nFZvwiPcddj9iMHMvWfAPf/f77x3+7EKytjpmXyPML/G0mYXVCdzNMfKgYDnWubdh9bMDuMD9upjcwcXgPWF5Hx39Ir/fW0rrVv/9A3++B/gNrCzN8Thc6O46gbOwMtDu/i7DykhMBpY15TFREIP5qvtZ8+e7sTqI31vu7mLAdZKHZ55/ziaXuz1+PSwB5mNlsdXfO9fjGH+Pe7rdCfyqh2ExlveH3T1kC5GbsPp61K9F2xHpNsvTLerGC/yZBVifdYc/s8PD2OvhjUF+CDK9WB/Qg02+tZHzMQZ5c7A25QayAN+LtSVhOfJUstB7HzagjgFkDADnkctC+J3Ig++HPd1iMP2oX59DLjO/8Dj0eFzXkwf93eR8P9nftYs8eDvF3YG1nS2YEAtWt8I66ID7/cd+71v+uYQ8Jtnn8Yk6ecifOeJp1erpEJNqEeYQJKNP2e9hDqF4cSlvXuppvJI8SReTBMc8vc8hC/sFNggPQfqV7ucmD9sdfv2L2CC82+MUAla0mY96/D/p7vZgZeg7Hqd2T9cWTNBPmCADJjAexvpnPF2i/2ojC+JRhqK9PeTuwjoJD19MrlWwctLin63+zqf7s2eQJyZbPD2in3qeP9NOnhSKtvO3/PupWD27ldzWnubvPZ/c/83zuEdbeI67X+Huo+7cho0rjwDP9vCsJLe5B8mWbtE39mJlB/c76kdBrhcFVudjwi3Sssfjtp88AbnA3Zzm74y+8V7y4e8ryGOTaCNj4maZh3++50tMwrZiZSQE1+hPwpIvrLUinWNSYCGW19E/LfV7YG3+fvc78vdKv7fWP99OHqOsbrj3Hvd3noe5hikaqlhZjDFHF3kiNMoZ5MmxGDPXyBNP55EnT56CjR3OwupdWOvF2DHGs09gZXIPVs5ehZXn15InIvDr64qiuA+gKIr9RVGE1WNzRnMI41T+A54GrC/9fgZ5VuxKbAB8FOuwjmCDrrXAv2EdQze543keNmALt+vd/xisrPd3bCFrNzaQZ2JjtvJurNPe4/5vI8+YPIc86/w41kH3YoOX53hYf+z+HyBrL7r8Hev9+1uwChkNZXnQWvi7o9JsJJuERsXtxQZd8Xx0uJF2ocWqYoW2hglO5XtrPX13eVyiUbrNP2NQvMn9vYcsnEbDErNZBxp+d3nahkAQ4Y4GPtyXtS8xaxaDhRjMPdjg9+NkjW5nye+j1M8KxyxhDAqqnsaRdkeBP/Pf73Y3H8FmgN9dincnNqsV2rEerCw8hs06xcxp5GXEN/IvwhgaiPKAPGZVy4PYiEMHeRARnWLM1oamLGadf+jhjZnabv8ds6TRaYRm8PvkwVPcf0kp3PF8FatfMQP5NOpndKMDjlntwtMkZgkjrpEmnyCXrcexenbM0/Niv76RXHZ3kutbjTyr+0X3v5z/0UbErO6PPI4/8O8x6Iy6tptcDmNiKfI20inytKf0OwYmx8j1sqP0bKX0nohHaHmi4+4u3SswjVMIrD1Y2xRa5i+V0jquRZwPldK+Wnp/PB8axXg+/h4gl+14NsJUblMiDrtLfsRntGmRPlGetpNnmcOvaB8iXaKtifQ4VroXWrew4vgfZE3tOqw+HyW3TzFAjs9Dfj807+U41Up/FfJMf3cpzxvrYtHkr1IKX1y7uyHdol04gGmB4l5j2nZTnz9FKc/K/UM89zbq27doK8p9yO83xDnC3AO8vyEtesmz9tG+3evh3o31s4dLfn+UXHYjn2MC4Kvk9j3Cdjt5Rn0vVn4f9N9f8WfCYuNhcjnfS+7/atjkXTkPQpNTYAO0iF/hbg82pPldJXeRztG2hma9XM/i+b/x52NAHRY0RSlNbmgIV2gnCmzyrjEfot7tJPcR5Xau/PxjDb/L7VCUiYh3uU2PuEU6XNPEn6iXR0rua9gYKcYnkcZ3YeWggk0e18gC73P8XqTrFrIVSAi866hvG8t5eaR0Ldq0bvdvT8Pz0deX62dPyZ/GOhvCZKRNtGVhAVJ+/hcNbhvrfPmzt+F+uT3d08R9f3kV/kR9bEyPGAPERGv0gZUmfjf2ATFmKZepY9SnW+T5o+QJonJZ7m54T7lva4xj47Woa41tXbPnm7XTjf6HBVGkYbk9vb/h2cZ4RJ73l7+NYQs/GsPSLFzlMtB4rdrwPdx3kC0JIqwxZtuBtXc3+t8a4C8Gk6dmpEaxkZTSAkyL9k+YoHcmNit0G3AFNiOxAtMw1bDZ5PMxFW0P1ng/jdzhNnvH32AZ0I2ZoC7HGrjN5AZ5HfBhf9dVZFPE2Vhj2YvN9vyph6MN01zeiGX8EqzSHcNm1Xa4v49jsw4fwgZ/nR6G55MbvhrWUD6Izfol4H+STR5/1d9/lUdpJ9a5HsMG/49hnfcqrLG6FWt0QmtUJa+l6ARe5HHZ5e99gqw1u8XdzvH0+mN3ux2bAdvtcQ0BMMzFQiD5nN/7BVaJ92KD9grwB2QTq++4X7PJs2Q3kE20ovw/VPr8GdmsEqxjmkueFY5ZyDbPs2VkbeKHMM3ObA9/K9nM4ieY5vNUf0/CTFDfQDZd2IxNENwG/IOnxfcwAWkjeX3jLe7+/f77bvJgaZOn1ffdryfJZpGHsRneaLi6PZ0SNhMY69Wehs0sF1hdiPWnBz0MTyF3fGHDH/XiVR7v8gDsBr83kHnDWdSvrYyZZ8ja/pe4v5eQ1wpcjuX7H5LXxxYNfp+B5ck/k4XBJf7sA/7McqzcvNDj1+bpdTfwLKyshtY1OtNn+vUF2KA1tNpPYpMlFf/djpW5mJnuxQZLnyGvHd2ClbloK75Jnqj6nIftMFZXf0GeRApB6Ij71Ya1ETFL/DvudiFZezHL/XozWQCOSgABGAAAESVJREFUvI+JrlgzfAm53dvsYYryu53cLoQZ0V1kc6kustlmT+le1KWHsRlesPob5SPWeke4Dvv1tdSvqwzzsvCrKN2/wMM4B5ukiTq/0D+PYuVpv6fZ7dhM7Rc9H2LW+AGs/lWwWe4wWY/wfZE8aIE8EPseeeDzbQ/T1z2Omz0Ma8lakSMeplhTXx4cXODP1Nzdzf67DWu3wdpkyNrvmJQIIs1izd87/Hcia9VjjfYWsrl9WCAcdXdhbnWNf0aa/1ds9h2sXQpB/U2lcOzyz4VYv/ZsrM4cxfLyP/uzN3p42zBt10bgUiyNb8TqTpVsnt+L9Ws1bEJ4MzZhGpq/e/36Q1jdmOvxXIyVz9Oxsvoh8gRjaHKfiy39KDz8X/c0+a6HfS02VriObC4a5stVzKok2slDWHm8yOP5X/xdoaE9is3+R39QYGOVELqijO335xf65wGsf2lx//d4GHd63vyUrP38HFlwX+BpFRNUEffQznS5+06y9cgerP3Y6+8/ii27CZPGiP9CdxdaqVlkAXG5P9vpafZ8d/eIp8lebGx0tad39AsPebhejo2l2v1dZ5O1uGBtSUy0zcPaj60eltAgrcPy/xjZbD40ghvJ2rY2TKBvI2vmIGvTIq1q7h/+7vlkDc5BrBxBnqiBLHSFJccqMmUhsoU8RtlKzqMQ2Df7vX/1z7B0uN3vg5Xh3Z4GoR1+v787TOE/635uwPJ5u/sd5Tzed79/XudhjOUR5b0obsbK+31Yf/oguf96yN2/BmuXI08q2Hi0io1Da9h4PSaM92J5/CN/zwa/F2XkSg9bBdNyd2Bj4yjvYZ4Z5eRycp9zNtn0v4qVoQJbtnKuu4mxxbX++e/uNqwYYhLnCDb+ikmSCja5F2OEJzwvbvXfD3v49nnadHh6hQAfY3jc/2jrw+Q9rI+inejC+rKwoAqlxVFP3+sxbep/xfqP304p/QYDMdmav/HWKGKV5kZPrA97QQiN3FGsw9uKrVd5L77mqOTHdqzQrccK9P/27xd5Jlzsz9yBCQIVsrYiZiJv94KynmzKeIGH673+3Hs9PNs9zLe420c8fD/EBrrdHv6yNugBrKGMTrQGrPQ4vN8LzpNYQX8/8L/c/Y0e7g9hppIxaIxO7hjWGOzCGoOYwSjPGDfOsDTOdjS71/hMzMYcw2aCP0DWAH0eq+z/jFWwrR7ubcDf+bVYl1HzsG9xf9Zj5moheDyJCUA/8mur/fo7/PcfupseTFirkmdvw/97sE6mF2sMCvK6tteT15n+pYfrHe7Pn2GN2B3YutlusplhpPU6/1tPXnf5554GH/PndmED7kNYGan476OYff9Ov9eoQb3F0/R3yVqCL3kZ+Qfyutxt/vuvPI7f9TB/BJtICWE9hJhjWKO6x+MTs7z7sUYv1mzWyOsfDnj8N5LXr8Y6ya3kstU4ExzrYC4il5uL3f+byJqcTZhGMbT12z2cF1M/kxzmMlXPmyh/ISzt8LivLoX7GNapxABtr6frX5E1ISFIhtZtM3n9Yy/WiXZ52m8ovb/bw13BBkk92MA2BOQYPEZ63EnWijfOWvansSr/DTSD2cyfWMcZZoz/UQr3Zn/2t/3eG6nXcka4Q+scGqEOv/cWfy7yJgTQWDcWnXZ5oFI2/YsZ/JiZvoDc9rzGr13v7j5JXrPziF8Ly4cXkrUHUX+ivsQan8NkS4un+TtiTVfU5/L6xUfJEzCN6R6DiTvdXbRX38UGEL/w9KqQ18q9nCyMbitdK1tWhJY7LGN2YWU6NIZhthxa+TDrjEmHSPuoI6HFLLfrcb8HM0ONd9/h6foEJojFIGs1WRNxD9kS5lFyvQkteVjjfNTDHWvJHsDaiFiTtAmrg/eS1wV3e3x/6GGI+hifkX87/ZkY0P7U3cUa1C73+0asXIYJ7H5/tmypEhqxHk/v6EOHWreG81fW3O3z8MbkT5hth4b1sKf9fk/nCHekdbRTd3m4w2w10ikG8lFWYh15jJ8i38IqqNbw1+3P7MDqcdT3Tf78T8jr2zvI2rherByFmfpjpTBH/xCTZZtL76p5foUFTLTJMYgO7WTZAiHqZ7lsV0ruy2X7Z1hbsdvT6WHyPhAx2ROT/vf5u7eWwn2QLEzs8vyL/vFfyIJalOVyPxPjmwNkE/+yVUn0ZWGK/E/kNuGjntYPeRocxvr0LR6GmNxYg01ghrl42aLsEFZntpPr8hGsr9rmfoUwtwMbVx4s+bUem7yP9iYsi3rJAtQ6D2cHNj7YhY1ZY+1xbMay1eOwlrz/wofIll2R7zUPW7TvZW1po1VIf33jcJ6pYJZ/uzChrNfjtJ3cVh3DJmwqmAVj2equLBw+4OkSy19iknsfuX2MNcthQlzBJiYrZFPvx8nt1AHMsm03sMrHIH8LvO9E1yh+HitUXyiK4q8xzcV6TPD4aVEUl2CF+nV+/YVAJaU0z90vxDq92PkotGaXkAckJ2GD6HVYRfwMVoifwBqx72EajPXYAApMaN2A2avvx2acjmEDhs+TtTQrsIZnB7YG4lOYJjDMI+8jz17FjO0x4MKU0nOwGd1dZI3dK7DZOjCT2r/za1v8uTUe3/3YYuVfI8/GhanBv2HC1l9iBfF+rNF5yMP1AFY5byuKYhY2iHkAm0HpwrRo92MFeSc2ACywzv7/xWZ6tvs73uzp9yZPmxUe7g53/ytYBVmNVZbw61ysYr6eXJE7sFnv/f776VgH9E5P6yuw2a/9nje3YwPxmHXcjVXQ0M7EbPvrMM3T+zzc13naXOPxOYitB1uGlZOLsI2EHidX5r/wz9gI4DWeR71YmXwJWYB5GjYDGGUyzHPOdffXedirfn0fec3iJeSNXA6mlH4dMzl7DJt93YxpoU5xv56DmXW+zvPnMHmjhh3YoPulntaLyDuk/cLjG4vpY2bzEDbzWMEGLaf69Rgk7CFvpoCn+V5Pm8X+/kv9XpW8Xutsf0fkTawNWYRN9BQeti7gr8nmp98krxepYZrwFqzML8fq4nOxDmixp9Nr3e/QBu7FNNlbyIPnG8kN8xkejwvdzRlAT0rp+VidDk1XD1kIfaX79TBWP0Mo6sTKZLen70pPk1i3dQTrYC/ydNnp/obmIASx0LTvIAt5sSnIJrJ25GX+2YnV426sLBfAf/L07cHKSwVr+7rd3w5sgqKbrFHsLIXtW+Q1jK8ma5Egr4PuJq/jiw06bvbfHyQP7jb4vdiB9xBZA7/C/bnAw/Faf9fmUvp/2+/t9b917t8Osjn1Yn9mD3lt9h/5O15O1pxVsPoR9XMNeSKqi6yViHYLrO72eDqAld+TsYHVr2L5vxxrm/7W/XshVrdj47UYTD7h4Y718POxtnyh+92NrSGKSZ3dnh5LPd1CUxProx/E2qTyjHVoaWKg9Ofk3XW3YOUiNOmzPUxPI6+ffIb7E5OnsRnQMfJkXauHM6xVerH2eafH5V5M+xrrnmd7vsRyhiN+7ace753kGfw2rM3eifVtp5FZRF6DetTDNs//HiUPzHdjfV4PeZlFrBmPzTdCKHocy8sa2YrhWn9uP1mgutrf9113+wOyEBf+3UR9m/pzrK+JPiHyKNJlGdZmR92KCY8Qitdj7c5cT7O55ImCuR7edup3b20nr6EPrW60MSGUR3pUsLqzjNxnzva03eh+xJrWY+S1sL/kabMH037ERGFMIh/wOC3xtIi1hmH9EXk0i6x9iXKUPG0OYXsNhNBYeLhjMq6GbfIS47Hl7udSsiVXpHm75+d5fm8upoEPzfbPyBuQzfM86SJrXX8DK0/Pcjft5KUzYWE1C2tPZpEnYyALCg+73/eQ+8lYgzcP01Af9e8Penzm+N8PPL4vJm/8F4JXrKM8ibwGco6H6SUexpWYcD8XG99eitXTSLuPY31ZbEbWQbZ4KrDy8Q/Y+K7d/duJjZkX+LWl2BjrNPK4ZpPn50qsTYgxVOxVsNz/biZvCFXFxma/oH6Zwas8LOU1/ndibesr/Not1Ct3ynX6Vqx/exwba/Vi+zbE2u2l5HHo49j49edk0/yYNFmPySpQX6ajn4x175EuCcvTCjZ2PEbe5Oml5AmYv8c0z8uAXSmlNqwff5ABSC5RzihSSldhA5wV5E0AFmGJuQeTqDdhg5ot5MQ+gGXqJ7G1GHOp3567h/rBzEAUNN86/XgwsUxtKz0bM2az+3EffnSTTcPK74p74Wd5W98uspCb+vE7ZmJPof64ighbFyaYrsAGu6HaDlOXpWRTjF7y4DUa93dhFW87VlCjMwsTjyjoYboYJh0tJX8b0yI2o4jOJ95blJ6PmbHI54hrDFrKeRAzTRGGGtbYnEkevMSMZnQMi7CO61lYY9CLDYqewDqSHg9brLtZTN684PnYrNjD2IApZirPcj+fTu6Qo/xUyRsHRT6Ww1+QZ6AW+2fkVZjdRdwjPSAvzIb+jx04htWZU8mbEkDz8iQmlv7anBM5b1Q+62lWHqKNHan7E72M9Zd+kS7DSd8ThXKZKfddg5Wj6EvL/jAEd8MN01jT6HdjmRhovDhYmGZy/RtJ3AZzE5NKlJ6LMd9AbmNsBX2PKRuLPBjLfIwxX2jID2JjvVB43ILtrFoA3y+K4i8G8mxGCopCCCGEEEIIIUaOZrmEEEIIIYQQQtQhQVEIIYQQQgghRB0SFIUQQgghhBBC1CFBUQghhBBCCCFEHRIUhRBCCCGEEELUIUFRCCGEAFJKH0gpvXcM/Ts7pXRvSmltSumZY+WvEEIIMRFIUBRCCCHGhzcA3ymK4vyiKDaPxwtSSq3j4a8QQgghQVEIIcQJS0rpb1JKG1NKPwJ+2a/9l5TS6pTSfSmlb6aU5qWUFqaUtqSUZvkzi1JKW1NKs1JKz0sp3ZlSWpdS+lZKaWlK6TXAnwJ/mFK6JaX0wZTSfyu99+9TSn/i39/n71uXUvpfpWe+nVK6J6X0QErpnaXrnSmlv0sp3QW8aGJSSgghxImGBEUhhBAnJCmlC4G3AOcDvwNc5LeuLYrioqIozgM2AH9QFMVh4CfAa/2ZtwDfLIqiF/h34C+LongucD/w/qIovg98Gvg/RVG8HPg88HZ/b4u7/2pK6VXAWcDzgecBF6aUfs3f8ftFUVwIrAL+JKW03K/PB9YXRfGCoih+PuYJI4QQQiBBUQghxInLS4FvFUVxtCiKQ8B1fv05KaWfpZTuB94KPNuv/xvwe/7994AvppQWA0uKovipX/8SEILecYqi2ArsTymdD7wKWFsUxX7//ipgLbAGOBsTHMGEw/uAO4HTS9erwDdHG3khhBBiINomOwBCCCHEJFI0uXYl8IaiKO5LKb0DeBlAURS3pZSellL6T0BrURTrXVAcKv8GvAN4CvAFv5aADxdF8ZnygymllwGvAF5UFMXRlNJPgDl+u6soiuow3iuEEEIMG2kUhRBCnKjcCvx2SmluSmkhcLFfXwjs8vWIb21w8+/AVcAXAYqi6AAOpJRe6vevAH5Kc74F/CZm4nqjX7sR+P2U0gKAlNJpKaWTgcXAARcSzwZeOLqoCiGEEMNDGkUhhBAnJEVRrEkpfR24F3gM+Jnf+lvgLr92PyY4Bl8FPoQJi8HbgU+nlOYBj5LNUxvf15NSugU4GBrBoihuSimdA9yRUgLoBN4G3AD8UUppHbARMz8VQgghJoxUFM2sboQQQgjRSErpEuD1RVFcMQK3Ldg6xEuLotg05oETQgghxhBpFIUQQoghkFL6GPBq4DUjcHsu8F1s8xwJiUIIIaY80igKIYQQQgghhKhDm9kIIYQQQgghhKhDgqIQQgghhBBCiDokKAohhBBCCCGEqEOCohBCCCGEEEKIOiQoCiGEEEIIIYSoQ4KiEEIIIYQQQog6/n9gup1sr9RitAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "rides['date'] = pd.to_datetime(rides['dteday'])\n",
    "rides['dayofyear'] = rides['date'].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(15,8))\n",
    "sn.pointplot(data=rides[['dayofyear','cnt','yr']],x='dayofyear',y='cnt',hue='yr',ax=ax)\n",
    "ax.set(title='dayly distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    每年的开始和结束一段时间内骑行数量少，中间时段骑行数量多，猜测骑行量和季节或月份月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 月份与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5243eca08>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=rides[['mnth','cnt']],x='mnth',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    利用violinplot函数可以看出每个月的详细分布，但差异不容易区分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=rides[['mnth','cnt']],x='mnth',y='cnt')\n",
    "ax.set(title='Monthly distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    利用barplot函数，通过均值可明显看出每月骑行量的差异"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 季节与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d52381f588>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=rides[['season','cnt']],x='season',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    每一季度的详细分布，可以看出，第2、3、4季度的骑行量高于第1季度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=rides[['season','cnt']],x='season',y='cnt')\n",
    "ax.set(title='Seasonly distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    从均值来看，第2、3、4季度的骑行量明显高于第1季度（barplot展示的是某种变量分布的均值）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Weathersit distribution of counts')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=rides[['weathersit','cnt']],x='weathersit',y='cnt')\n",
    "ax.set(title='Weathersit distribution of counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    可看出，在（1：晴天，多云，2：雾天，阴天，3：小雪，小雨，4：大雨，大雪，大雾）四种条件下，骑行量逐渐降低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d525c81388>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(ncols=2,figsize=(15,6))\n",
    "sn.barplot(data=rides[['holiday','cnt']],x='holiday',y='cnt',ax=ax[0])\n",
    "sn.barplot(data=rides[['workingday','cnt']],x='workingday',y='cnt',ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    可以看出，非节假日的时候，骑行量稍微多一些"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 数值特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d5269402c8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = rides[['temp','atemp','hum','windspeed','casual','registered','cnt']].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    可以看出：温度与骑行量的相关性比较大；天气温度和体感温度有很大的相关性；注册用户骑行量比较大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.对类别型特征进行one-hot编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>...</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  mnth_4  \\\n",
       "0         1         0         0         0       1       0       0       0   \n",
       "1         1         0         0         0       1       0       0       0   \n",
       "2         1         0         0         0       1       0       0       0   \n",
       "3         1         0         0         0       1       0       0       0   \n",
       "4         1         0         0         0       1       0       0       0   \n",
       "\n",
       "   mnth_5  mnth_6  ...  weathersit_1  weathersit_2  weathersit_3  weekday_0  \\\n",
       "0       0       0  ...             0             1             0          0   \n",
       "1       0       0  ...             0             1             0          1   \n",
       "2       0       0  ...             1             0             0          0   \n",
       "3       0       0  ...             1             0             0          0   \n",
       "4       0       0  ...             1             0             0          0   \n",
       "\n",
       "   weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          1  \n",
       "1          0          0          0          0          0          0  \n",
       "2          1          0          0          0          0          0  \n",
       "3          0          1          0          0          0          0  \n",
       "4          0          0          1          0          0          0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    rides[col] = rides[col].astype('object')\n",
    "    \n",
    "X_rides_cat = rides[categorical_features]\n",
    "X_rides_cat = pd.get_dummies(X_rides_cat)\n",
    "X_rides_cat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.对数值型特征进行MinMaxScaler，去量纲"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.355170  0.373517  0.828620   0.284606\n",
       "1  0.379232  0.360541  0.715771   0.466215\n",
       "2  0.171000  0.144830  0.449638   0.465740\n",
       "3  0.175530  0.174649  0.607131   0.284297\n",
       "4  0.209120  0.197158  0.449313   0.339143"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#感觉数据已经做过处理（取值都在0-1之间），这里用MinMaxScaler再处理一次\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(rides[numerical_features])\n",
    "\n",
    "X_rides_num = pd.DataFrame(data=temp, columns=numerical_features, index=rides.index)\n",
    "X_rides_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.将处理后的数据进行整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.1 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.355170</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.828620</td>\n",
       "      <td>0.284606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.715771</td>\n",
       "      <td>0.466215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.449638</td>\n",
       "      <td>0.465740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.175530</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.607131</td>\n",
       "      <td>0.284297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.209120</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.449313</td>\n",
       "      <td>0.339143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season_1  season_2  season_3  season_4  mnth_1  mnth_2  mnth_3  \\\n",
       "0        1         1         0         0         0       1       0       0   \n",
       "1        2         1         0         0         0       1       0       0   \n",
       "2        3         1         0         0         0       1       0       0   \n",
       "3        4         1         0         0         0       1       0       0   \n",
       "4        5         1         0         0         0       1       0       0   \n",
       "\n",
       "   mnth_4  mnth_5  ...  weekday_5  weekday_6      temp     atemp       hum  \\\n",
       "0       0       0  ...          0          1  0.355170  0.373517  0.828620   \n",
       "1       0       0  ...          0          0  0.379232  0.360541  0.715771   \n",
       "2       0       0  ...          0          0  0.171000  0.144830  0.449638   \n",
       "3       0       0  ...          0          0  0.175530  0.174649  0.607131   \n",
       "4       0       0  ...          0          0  0.209120  0.197158  0.449313   \n",
       "\n",
       "   windspeed  holiday  workingday  yr   cnt  \n",
       "0   0.284606        0           0   0   985  \n",
       "1   0.466215        0           0   0   801  \n",
       "2   0.465740        0           1   0  1349  \n",
       "3   0.284297        0           1   0  1562  \n",
       "4   0.339143        0           1   0  1600  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FE_rides = pd.concat([rides['instant'],X_rides_cat,X_rides_num,rides['holiday'],rides['workingday'],rides['yr'],rides['cnt']], axis = 1)\n",
    "FE_rides.to_csv('FE_day.csv', index=False)\n",
    "FE_rides.info()\n",
    "FE_rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 五、模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.将数据进行分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#读取整理后的数据\n",
    "df = pd.read_csv('FE_day.csv')\n",
    "\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['cnt']\n",
    "X = df.drop(['cnt'], axis = 1)\n",
    "\n",
    "# 特征名称，用于后续显示权重系数对应的特征\n",
    "feature_names = X.columns\n",
    "\n",
    "# 将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，80%作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作业-对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.模型选择并训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 最小二乘线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>4550.708897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2654.792827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>1287.992360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>995.293778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>929.887649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.410909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>830.579518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>586.296405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>517.803038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>409.589079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>407.138803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>238.417312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>216.956549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>189.797216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>85.141889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>77.516263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>66.464787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>43.650546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>5.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-6.919060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-31.943212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-130.807162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.612446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-202.493250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-203.137582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-263.761415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-485.714239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-541.439917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-784.914245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1174.845790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-1207.111892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1298.592138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1323.999988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1486.521042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "33            yr  4550.708897\n",
       "27          temp  2654.792827\n",
       "13        mnth_9  1287.992360\n",
       "28         atemp   995.293778\n",
       "14       mnth_10   929.887649\n",
       "17  weathersit_1   914.410909\n",
       "4       season_4   830.579518\n",
       "16       mnth_12   586.296405\n",
       "12        mnth_8   517.803038\n",
       "18  weathersit_2   409.589079\n",
       "15       mnth_11   407.138803\n",
       "26     weekday_6   238.417312\n",
       "32    workingday   216.956549\n",
       "10        mnth_6   189.797216\n",
       "2       season_2    85.141889\n",
       "25     weekday_5    77.516263\n",
       "23     weekday_3    66.464787\n",
       "24     weekday_4    43.650546\n",
       "9         mnth_5     5.009200\n",
       "0        instant    -6.919060\n",
       "22     weekday_2   -31.943212\n",
       "3       season_3  -130.807162\n",
       "20     weekday_0  -191.612446\n",
       "21     weekday_1  -202.493250\n",
       "11        mnth_7  -203.137582\n",
       "31       holiday  -263.761415\n",
       "8         mnth_4  -485.714239\n",
       "7         mnth_3  -541.439917\n",
       "1       season_1  -784.914245\n",
       "30     windspeed -1174.845790\n",
       "6         mnth_2 -1207.111892\n",
       "29           hum -1298.592138\n",
       "19  weathersit_3 -1323.999988\n",
       "5         mnth_1 -1486.521042"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs_1 = pd.DataFrame({'columns':list(feature_names), 'coef':list((lr.coef_.T))})\n",
    "fs_1.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用RMSE对模型进行评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LinearRegression on test_dateset is 814.4749076863644\n",
      "The RMSE of LinearRegression on train_dataset is 742.7543512758713\n"
     ]
    }
   ],
   "source": [
    "# 按作业要求，使用RMSE对模型进行评价\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('The RMSE of LinearRegression on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_lr)))\n",
    "print('The RMSE of LinearRegression on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_lr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>1778.493414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1546.374034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>1504.623924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>914.843092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>767.205317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>678.352931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>388.865215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>387.713628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>369.663154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>298.550050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>227.515740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>212.558710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>205.686009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>110.998614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>106.306513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>84.873020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>81.396550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>62.795850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>59.857933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>1.417157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-34.140080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-91.388275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-143.125249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-191.851510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-194.714350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-205.574484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-242.548582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-248.222941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-725.828529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-786.815656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-824.928596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1087.773198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1142.397276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1303.708307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "27          temp  1778.493414\n",
       "28         atemp  1546.374034\n",
       "33            yr  1504.623924\n",
       "17  weathersit_1   914.843092\n",
       "4       season_4   767.205317\n",
       "13        mnth_9   678.352931\n",
       "18  weathersit_2   388.865215\n",
       "9         mnth_5   387.713628\n",
       "10        mnth_6   369.663154\n",
       "7         mnth_3   298.550050\n",
       "26     weekday_6   227.515740\n",
       "32    workingday   212.558710\n",
       "12        mnth_8   205.686009\n",
       "2       season_2   110.998614\n",
       "8         mnth_4   106.306513\n",
       "14       mnth_10    84.873020\n",
       "25     weekday_5    81.396550\n",
       "24     weekday_4    62.795850\n",
       "23     weekday_3    59.857933\n",
       "0        instant     1.417157\n",
       "22     weekday_2   -34.140080\n",
       "3       season_3   -91.388275\n",
       "6         mnth_2  -143.125249\n",
       "20     weekday_0  -191.851510\n",
       "5         mnth_1  -194.714350\n",
       "21     weekday_1  -205.574484\n",
       "11        mnth_7  -242.548582\n",
       "31       holiday  -248.222941\n",
       "15       mnth_11  -725.828529\n",
       "1       season_1  -786.815656\n",
       "16       mnth_12  -824.928596\n",
       "30     windspeed -1087.773198\n",
       "29           hum -1142.397276\n",
       "19  weathersit_3 -1303.708307"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "# 2.生成一个 RidgeCV 实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "# 3.模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "# 4.用训练好的模型对测试集进行预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs_2 = pd.DataFrame({'columns':list(feature_names), 'coef':list((ridge.coef_.T))})\n",
    "fs_2.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用RMSE对模型进行评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of RidgeCV on test_dateset is 812.0412683712976\n",
      "The RMSE of RidgeCV on train_dataset is 747.4410131599817\n"
     ]
    }
   ],
   "source": [
    "# 按作业要求，使用RMSE对模型进行评价\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('The RMSE of RidgeCV on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_ridge)))\n",
    "print('The RMSE of RidgeCV on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_ridge)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 Lasso模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 128155765.49747854, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124237097.47580852, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124404407.6562402, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126641504.8447259, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126483662.02624018, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126350069.07426925, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126266143.55127867, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126217003.07178046, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126189068.30268449, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126173434.84491515, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126164765.96900982, tolerance: 176331.28855032122\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 132632850.85720974, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124714698.69332716, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 120788349.30547287, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129516664.51149547, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129704965.73518734, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129629189.97557108, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129564524.30552153, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129522486.75465856, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129497203.48293415, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129482583.02914564, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 129474305.19280961, tolerance: 171396.26788522486\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 126053809.22215009, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 121793014.04183862, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123771931.13141032, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 122613042.58713886, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123229887.84016082, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123190485.5363854, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123130273.9162689, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123088066.43075188, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123062050.42117825, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123046783.82691577, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123038049.6021552, tolerance: 178645.74666680943\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 113890036.24356376, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 127014479.84933357, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124850653.17076021, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 125058612.78548665, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124973133.14978318, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124870075.21183431, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124799682.73309267, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124756900.65551946, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124731941.70588616, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124717666.95888215, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124709598.45341566, tolerance: 172687.4240346895\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 46744148.91845173, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 116033477.35881269, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123540252.2284336, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 110366757.88672164, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124837989.24853577, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124909105.40988377, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124796809.80135812, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124716455.51166569, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124667318.54325365, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124638774.130823, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124622593.50363544, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:472: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 124613547.54762678, tolerance: 169474.6999863248\n",
      "  tol, rng, random, positive)\n",
      "c:\\python\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 162360641.58816293, tolerance: 217175.10367928082\n",
      "  positive)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>yr</td>\n",
       "      <td>3421.928302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>temp</td>\n",
       "      <td>2604.778038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>atemp</td>\n",
       "      <td>1031.216769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>985.968929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>season_4</td>\n",
       "      <td>762.656808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>529.172378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>weathersit_1</td>\n",
       "      <td>499.786022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>311.142065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>175.522147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>88.417062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>weekday_5</td>\n",
       "      <td>28.337667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>workingday</td>\n",
       "      <td>26.650798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>weekday_3</td>\n",
       "      <td>16.844192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>season_2</td>\n",
       "      <td>6.484584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>weekday_6</td>\n",
       "      <td>1.192137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>weathersit_2</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>weekday_4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instant</td>\n",
       "      <td>-3.833660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>weekday_2</td>\n",
       "      <td>-80.291681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-89.722164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>season_3</td>\n",
       "      <td>-196.975386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>weekday_1</td>\n",
       "      <td>-249.666332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>-269.962361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>-308.206221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-314.454689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>weekday_0</td>\n",
       "      <td>-427.132256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-444.961683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-846.672551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>season_1</td>\n",
       "      <td>-863.189035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-1035.088960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1179.804870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>hum</td>\n",
       "      <td>-1313.807973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>weathersit_3</td>\n",
       "      <td>-1729.938672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns         coef\n",
       "33            yr  3421.928302\n",
       "27          temp  2604.778038\n",
       "28         atemp  1031.216769\n",
       "13        mnth_9   985.968929\n",
       "4       season_4   762.656808\n",
       "14       mnth_10   529.172378\n",
       "17  weathersit_1   499.786022\n",
       "12        mnth_8   311.142065\n",
       "10        mnth_6   175.522147\n",
       "9         mnth_5    88.417062\n",
       "25     weekday_5    28.337667\n",
       "32    workingday    26.650798\n",
       "23     weekday_3    16.844192\n",
       "2       season_2     6.484584\n",
       "26     weekday_6     1.192137\n",
       "16       mnth_12     0.000000\n",
       "18  weathersit_2     0.000000\n",
       "24     weekday_4     0.000000\n",
       "0        instant    -3.833660\n",
       "22     weekday_2   -80.291681\n",
       "15       mnth_11   -89.722164\n",
       "3       season_3  -196.975386\n",
       "21     weekday_1  -249.666332\n",
       "7         mnth_3  -269.962361\n",
       "8         mnth_4  -308.206221\n",
       "11        mnth_7  -314.454689\n",
       "20     weekday_0  -427.132256\n",
       "31       holiday  -444.961683\n",
       "6         mnth_2  -846.672551\n",
       "1       season_1  -863.189035\n",
       "5         mnth_1 -1035.088960\n",
       "30     windspeed -1179.804870\n",
       "29           hum -1313.807973\n",
       "19  weathersit_3 -1729.938672"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "# 1.设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#Lasso可以自动确定最大的alpha，所以另一种设置alpha的方式是设置最小的alpha值（eps） 和 超参数的数目（n_alphas），\n",
    "#然后LassoCV对最小值和最大值之间在log域上均匀取值n_alphas个\n",
    "#np.logspace(np.log10(alpha_max * eps), np.log10(alpha_max),num=n_alphas)[::-1]\n",
    "alphas = np.logspace(-3,2,20)\n",
    "\n",
    "# 2.生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV(alphas=alphas)  \n",
    "\n",
    "# 3.训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "# 4.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 看看各特征的权重系数(coef指系数)，系数的绝对值大小可视为该特征的重要性\n",
    "fs_3 = pd.DataFrame({'columns':list(feature_names), 'coef':list((lasso.coef_.T))})\n",
    "fs_3.sort_values(by=['coef'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用RMSE对模型进行评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RMSE of LassoCV on test_dateset is 813.4588856729789\n",
      "The RMSE of LassoCV on train_dataset is 743.2332703812754\n"
     ]
    }
   ],
   "source": [
    "# 按作业要求，使用RMSE对模型进行评价\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print('The RMSE of LassoCV on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_lasso)))\n",
    "print('The RMSE of LassoCV on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_lasso)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 六、比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1）通过比较上述三种模型得到的各特征的系数可以看出，岭回归和Lasso回归都能使得线性回归系数收缩，并且在Lasso回归中有的特征系数为0。<br />\n",
    "  &emsp;&ensp;回归系数收缩是因为岭回归和Lasso回归引入了正则项，从一定程度上限制了特征系数的取值。<br /> \n",
    "  &emsp;&ensp;Lasso回归中某些特征的系数为0，是因为对于L1正则，目标函数求的是次梯度，当梯度在次梯度集合内的时候，该维度的特征系数为0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小二乘评价结果：\n",
      "The RMSE of LinearRegression on test_dateset is 814.4749076863644\n",
      "The RMSE of LinearRegression on train_dataset is 742.7543512758713\n",
      "岭回归评价结果：\n",
      "The RMSE of RidgeCV on test_dateset is 812.0412683712976\n",
      "The RMSE of RidgeCV on train_dataset is 747.4410131599817\n",
      "Lasso评价结果：\n",
      "The RMSE of LassoCV on test_dateset is 813.4588856729789\n",
      "The RMSE of LassoCV on train_dataset is 743.2332703812754\n"
     ]
    }
   ],
   "source": [
    "print('最小二乘评价结果：')\n",
    "print('The RMSE of LinearRegression on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_lr)))\n",
    "print('The RMSE of LinearRegression on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_lr)))\n",
    "print('岭回归评价结果：')\n",
    "print('The RMSE of RidgeCV on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_ridge)))\n",
    "print('The RMSE of RidgeCV on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_ridge)))\n",
    "print('Lasso评价结果：')\n",
    "print('The RMSE of LassoCV on test_dateset is', np.sqrt(mean_squared_error(y_test, y_test_pred_lasso)))\n",
    "print('The RMSE of LassoCV on train_dataset is', np.sqrt(mean_squared_error(y_train, y_train_pred_lasso)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)通过观RMSE评价指标可以看出：<br />\n",
    "&emsp;在测试集上评价由好到差依次是岭回归模型、Lasso模型、最小二乘线性回归，而在训练集上正好相反。因为岭回归和Lasso是在最小二乘线性回归<br /> \n",
    "&emsp;基础上加入了正则项，增强了模型的泛化能力，防止了模型过拟合的问题，所以效果要更好些。<br /> \n",
    "&emsp;根据各特征之间的相关性可知，'temp'和'atemp'之间的相关性比较高，此时输入特征之间存在很强的共线性，因此使用L2正则效果要更好，所以岭<br /> &emsp;回归要比Lasso好一些。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
